NAI NorCal

nainorcal

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords: Quantum heat engines, quantum phase transitions, Kibble Zurek scaling 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords : Quantum heat engines, quantum phase transitions, Kibble Zurek scaling Keywords 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Abstract and Introduction

The Bitcoin halving is slated for sometime around April 19th, 2024. As you might have heard, it follows the Bitcoin supply equation; which is a geometric series terminating after 32 divisions or halvings. In April 2024, we shall have the 4th halving, taking us from 6.25 to 3.125 mined per 10 minutes. BTC The last satoshi shall be mined 32 * 4 years from 2009, which shall be the year 2137. Below is a curve I made in MS Excel modeling the Bitcoin supply equation; Interestingly, however, 3 other equations look like the Bitcoin halving. One equation is behind the most profitable options trading algorithm in the world, one is behind the most powerful energy resource in the world, and one is behind the ability to find the energy in any physical system. More on these below: 1. The Black-Scholes equation The Black-Scholes equation is behind most of the profitable options trading in the world, including Exchange Traded Funds (ETFs) which can be traded like regular options. Multinational companies like BlackRock, which manages trillions of dollars in assets, recently launched a successful product for its investors. Bitcoin ETF Notice the term at the end, Assume an equilibrium situation with: Let the constant number be 5. Thus, we have: Let r be 2 and model as, When graphed, this behaves like the Bitcoin halving, as seen below. Here are similar graphs from other people’s research. 2. The Radioactive Decay equation Radioactivity is the spontaneous decay of heavy, unstable atomic nuclei to form stable nuclei. It is the power behind nuclear fission, the most powerful energy resource on the planet. Interestingly, while radioactivity is so good at releasing gargantuan amounts of energy, Bitcoin is good at absorbing it. But even more, both the Bitcoin halving and the radioactive decay process are exponential functions whose graphs grow in the same way. Consider the radioactive decay equation; The formula is easy enough to be taught to 17-year-olds studying A-level mathematics in High Schools. Now, represents the amount left. If this was Bitcoin, would be the bitcoins left to be mined. A A How about if we track the amount decayed, just like the bitcoins already mined? Easy enough. Subtract from thus; A A_0, Which we can see is similar to what we had before with the Black-Scholes equation. Taking A_0 = 5 and λ = 2, it is the same deal with us modelling y = 5 (1 - e^(-2x) ), hence a similar graph. 3. The Hamiltonian The Hamiltonian, of a mechanical system is defined as the amount of energy contained in the system. H While Bitcoin might be a digital computational system, from what Alan Turing describes in his thesis on a Universal Turing Machine, we can represent any computational system as a mechanical system. Therefore, Bitcoin can be represented as a mechanical computational system. Not that we have the resources to do it on earth. By further extension, classical mechanics can be described in terms of quantum mechanics, hence Bitcoin is a quantum-mechanical computational system. Thus, Bitcoin has a Hamiltonian unknown to us, that can describe its minimum workings absent external complexities and fluctuations in the number of users and miners joining and leaving the network. At the nanoscale, where Bitcoin’s digital dance of the halving resides, the simplest Hamiltonian is the quantum Hamiltonian, represented by hat{H} below. We can see this Hamiltonian operator is part of Schrödinger’s wave equation, fundamental to relating the Hamiltonian with the state of a quantum system at time ; t Notice the term: which is similar to: above. Using Euler’s formula, The introduction of the imaginary component has thrown us off the beaten path of a nice exponential leveling off like the Bitcoin supply equation.  We instead get a circle  with an angle given by and radius R = 1. i Ht Now let radius R = 1, and take |r| e^(-iθ) to be the common ratio of a geometric series with r being a complex number, |r| < 1 and first term a = 1. The resulting geometric series; can be modeled as a Fourier series hence the graphic below with r = 0.5,  a = 1 forming a circle of radius R = 4/3. https://en.wikipedia.org/wiki/File:Complex_geometric_series_animation.ogv?embedable=true#filelinks But what if radius R = 1, as we want? What would common ratio r be? First, we need to know that for the general form of a geometric progression; + + + + + … a ar ar^2 ar^3 ar^4 the sum to n terms, for |r| < 1. The sum to n terms then converges to a unique value as n to infinity. This is because r^n to 0. tends tends Hence, we can summarise as below; Note: Because Bitcoin's supply is capped at 21,000,000 it moves in a circle! This via circularity leveraging its monetary transactions velocity, V, in Irving Fischer's equation → MV = PT *** More wonders From the following are related to the Bitcoin halving; https://en.wikipedia.org/wiki/Geometric_series The expansion of the universe, where the common ratio r is defined by Hubble's constant. The decay of radioactive carbon-14 atoms, where the common ratio r is defined by the half-life of carbon-14. Last but not least, One of my Hackernoon post’s viewership statistics.

The Mathematical Mysteries of Bitcoin's Halving

বিটকয়েনের অর্ধেক হওয়ার গাণিতিক রহস্য

Die mathematischen Geheimnisse der Bitcoin-Halbierung

Los misterios matemáticos de la reducción a la mitad de Bitcoin

Les mystères mathématiques de la réduction de moitié du Bitcoin

बिटकॉइन को आधा करने का गणितीय रहस्य

ビットコインの半減期の数学的謎

비트코인 반감기의 수학적 미스터리

Os mistérios matemáticos da redução do Bitcoin pela metade

Математические тайны халвинга биткойна

Bitcoin Yarılanmasının Matematiksel Gizemleri

Những bí ẩn toán học về việc giảm một nửa Bitcoin

比特币减半的数学之谜

Bitcoin is a fire breathing Dragon, A Quantum Computer, an Ice King. Fire makes the economy go round, and Ice brings on necessary winters that reset the planet for the next crop. The hotter the bitcoin, the more secure The colder the qubit, the more pure Exponentially they run in unique directions. Exponentially they become ever more useful. Humanity is lucky to have Bitcoin and Quantum Technologies. I am lucky that these two are destined for marriage. Converging at the nanoscale to create me, and you, an infinity stone. Circa 2100 Circa 2100 CE, it will be the 22nd century. Nobody knows what will happen then. But I know two things, quantum computational technologies will be more mature, and Bitcoin will also be more mature. They shall no longer be teenagers; they shall be wise sages then. Powerful sages. The maturity will come in many forms. For Bitcoin, a lot more people will be orange-pilled. When the US Dollar Debt balloons to a nice $100 trillion, you can be sure illions of people will be Bitcoiners. B It is incredible. If any country on earth spent , its economy would soon be in hyperinflationary hell, to collapse not even a year later. But if that country is the USA, via the Federal Reserve, it enjoys a wide swimming pool thanks to global reserve currency status. $1 trillion dollars of debt every 100 days But that is not all. The US Dollar also enjoys a ‘ ’ in the form of investments flowing in from outside the USA or its US Dollar.  I mean, most Bitcoiners are Americans, so Bitcoin is keeping the US Dollar afloat! Dollar Milkshake Let that sink in. *** So, circa 2100, the US Dollar still rules the Iron Throne. It keeps growing big and strong from all the milkshakes it enjoys, and then there is the one provided by Artificial Intelligence, feeding on our internet data. At $100 trillion in debt, subways in New York City are now rags, woke-ism is at an all-time high (ATH) and millions of American people choose to “identify” as young, rich, and very beautiful. In Africa, the oil is nearly gone, but the poverty still stinks. The energy transition is still mostly a theoretical idea, alternations between mild heat waves and raging rainstorms visit everyone who lives in the tropics. The Cold War between Russia and the USA, started in 1947, still goes on. Occasionally becoming hot every other while, to the detriment of other people. This is a ystopia. D At least burgers are still cheap (they contain insects now), and many AI data servers have been relocated to the moon to allow human beings some peace and quiet. These servers would be buzzing all over the place like bees (that don’t make honey). Unsurprisingly, Bitcoiners have decided they need another freedom technology on their hands. Bitcoin is being milked by AI, for AI ensures taxes are paid in lossy inflationary dollars. Nobody still thinks the bubble will burst. Maybe the can be kicked as far into the future as our imaginations allow us. can Bitcoiners learn about quantum computing big time. And underground start-ups are started. Quantum Hacking the World’s Problems A Short Story It is Monday 15th March, 2100. Finally, the password-cracking monster is created. Call it QKraken. The scientist responsible for this works for a pretty huge company under the ground, off books, and off signals. Code name - Q After cranking in parallel universes for 8 hours, the QC spits out the key to one of Satoshi's earliest wallet addresses. There they are - 50BTC. 500 million dollars. He almost pees his pants. "Should I call HQ?" HQ is sleeping. Nobody works past midnight or this early on a Monday. It won’t be rewarded (damned ballooning taxes). So, 8 hours of work on Monday, in the 22nd century, is enough work as it is. "Should I freaking spend some??" He tries to think, but it is not happening. SHA256 and RSA are now toast! His team can finally crack open decades of secrets shored up by millions of people, over the course of decades! Jesus. This is nuclear! Q tries to think but it is not happening. Computing game theoretic analyses that would make a supercomputer freeze out, he falls asleep at the computer. And he has this strange dream: It is Judgement Day, like in the Terminator movies. Someone, perhaps the Russians, get tired of the monolithic power wielded by the US Dollar and its huge AI dogs, so one of them presses the button. Or is it the Joker from the Batman movies? The countdown is started, and nobody is around who remembers how to press the kill switch. This is it. This is the Great Filter. Fate is going to reset the clock. Take human civilization back to the Stone Age. The nukes fall fast and in the thousands. Nothing can stop them. In a few minutes, their parabolic trajectories will be through. Then, hell will engulf the earth. They all fall harmlessly to the ground. Like dead weights. Why, what happened? … “Q” He wakes up. A little frightened. “You fell asleep at your desk, are you alright?” “Yes…yes, I’m alright” - wiping slight drool from his mouth. Vivian looks at him with pitiful eyes. Q isn’t sure if he even cracked SHA256. He wants to check again, but he is too tired. Did he stop a nuclear fallout? Did he destroy encryption? He decides to go out and check. Forgetting he could just turn on his phone and check if WhatsApp is no longer encrypted. He gets into the street. It is a usual Monday morning. Nothing unusual. He needs a break. He goes home. There is yet enough time to save the world. Of that, he feels sure. *** Shor-GPT In a good world, people will want Shor-GPT (a spin-off of ChatGPT). For they are accruing massive amounts of files and accounts whose passwords they never remember. *** Being a Bitcoiner who loves quantum computers, is like being a nuclear scientist who loves world peace. At the surface, it is impossible. Then you dig a little deeper and behold, only nuclear power shall cure energy poverty and the cycles of currency inflation rooted in playing the economy like a fiddle (because more useful energy, not more money, creates value in an economy). If anything, we should perfect playing the atom like a fiddle, not fellow humanity. It is kind of the same thing with quantum computers. Yeah yeah, Shor’s algorithm might “one day” hack Bitcoin’s encryption algorithms. Until you realize quantum computers are too at the moment. busy kicking their own butts . Like Spot But given that Shor’s algorithm (with its logarithmic speed) is stronger than noble quantum algorithms like Grover’s search which could be used for Bitcoin mining (using only quadratic speed), then the reality of hacking encryption is something we need to find good and profitable uses for else it will remain simply destructive. The first thing is the need, and the second is the cost. Even today, encryption can be hacked at many levels if you have a need and can provide the cost. Say you lose the password to your laptop, hacking it is a good thing. Say you lose the password to your phone with $50 million worth of BTC; hacking it is a good thing. You don’t care if the process will use a CPU or a QPU or a prayer; you want to open the phone. This is ethical hacking. Like crime, unethical hacking spreads when social structures become oppressive to their citizens. Massive debt burdens - like $100 trillion - are oppressive. So the drug use, the crime, and the unethical hacking will only get worse. Whether it be with classical, quantum, or social hacking technologies. The shabbiness will get worse, and our only hope is to take up laptops and build classical, quantum, social code. If somebody doesn’t want nefarious characters who build selfish quantum hacking tech, then they should not frustrate plus oppress their poor parents while these characters are still children. So flip it on its head, and it is a glorious revolution. Hacking, once again, becomes discovery. It is discovery. We hack away rocks and find gold, oil, and fossilized dinosaur bones. Hacking is breaking new ground. Adult humans become alike little children, breaking open their toys to see what makes them tick. So that they can buidl even better toys. (Aside - This is why I destroyed many of my childhood toy cars). “Truly I tell you, unless you change and become like little children, you will never enter the kingdom of heaven” - Matthew 18:3 (Holy Bible) Heaven, Utopia, awaits us if we change our ways. Awaits us if we acknowledge that ice and fire, Bitcoin and Quantum Computers, are no enemies. They are two great creatures with great use cases. And we need to tame them both. Or they will tame us.

A Tale of Ice and Fire

The future of AI chips is about more than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI. The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have already embraced GenAI in their operations, with nearly half preparing to invest in it. reportedly What’s powering it all – AI chips – used to receive less attention. Up to the moment, OpenAI’s for a “wildly-ambitious” tech project to boost the world’s chip capacity. Sam Altman claimed he wants to raise up to $7 trillion and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities. Geopolitics According to a , the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics, and AI chip innovation all contribute to this forecast. recent study by IMARC Few people have more insights to share on AI hardware than . In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, the future of AI chips and emerging directions like analog and optical computing, and much more. Tony Pialis, CEO and co-founder of Alphawave An AI Leader Behind the Scenes Pialis is a semiconductor serial entrepreneur. He has previously founded and successfully sold two startups: Snowbush Microelectronics and V Semiconductor Inc. V Semiconductor was acquired by Intel in 2012, where Pialis spent a few years as the Vice President of Analog and Mixed-Signal IP. Pialis co-founded AlphaWave in 2017 with the goal of becoming “the next great semiconductor company.” Alphawave went public in 2021 with an impressive market cap of $4.5 billion. The company’s core products include silicon IP, chips, custom silicon, and high-speed interconnect technology tailored for major hyper-scaler customers like Google, Microsoft, Amazon, and Meta. Alphawave is an AI leader behind the scenes, powering hyper scalers and founded on a couple of key premises that Pialis laid out. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true , the major players in terms of scaling out data centers and computing these days are no longer the Ciscos of the world. It’s transitioned to Hyperscalers – the Googles, Microsofts, Amazons, and Metas. Hyperscalers have internal design capability, and they also build their own servers, networks, data centers, and campuses. First , the major challenge facing AI moving forward is not computing. Pialis’ view is that the ability to design and implement compute is there. The challenge is the connectivity needed to connect the compute in order to process all of the data. This is what AlphaWave is focusing on. Second The Explosion of Specialized AI Hardware While consumer apps like ChatGPT drove the initial hype cycle in 2023, enterprise adoption reports are mixed. However, according to Pialis, the AI semiconductor sector has seen an enormous explosion of investment and new in the second half of 2023 across industries and geographies. design starts Pialis cited major national initiatives around building domestic AI chip capabilities in the US, Canada, UK, France, Germany, Korea, Taiwan and Japan. After years of relying largely on NVIDIA, governments want to foster their own chip industries to reduce strategic dependence on any one vendor. While NVIDIA CEO Jensen Huang also says that , it does not seem like this also includes the hardware layer. every country needs Sovereign AI Pialis believes this surging demand is spurring not just startups but also tech giants to develop specialized training and inference hardware. In his view, not every organization could or should develop its own AI model, but that’s bound to change. “Over time, AI will inevitably progress to be something like a utility, where hyperscalers will provide access to all of the compute power, like electricity. I think it will be reasonably priced. Anyone will be able to use this utility to train, develop, optimize and deploy their own models. But there’s going to be a lot of profit taking between now and then, and who knows how long it’ll take to eventually reach that state”, said Pialis. Leaders, Dark Horses, and Rising Stars in the AI Chip Race Few people would disagree that NVIDIA is clearly the dominant force in AI accelerators today, and Pialis shares this view. He sees AMD as a strong second place and praised CEO Lisa Su for her leadership. Others such as Ben Lorica also see . favorable winds for AMD in the GenAI chip market But Pialis cautioned not to underestimate Intel, calling it a dark horse that could still prove highly formidable in this market. More specifically, Pialis spoke highly of . Intel’s Habana acqui-hired division led by David Dahan Having worked at Intel, Pialis has an insider view. Having , and kept track of their , we tend to agree. Dahan helped architect new Intel processors that have showcased performance exceeding NVIDIA’s latest GPUs on key benchmarks. covered Habana, met Dahan MLPerf results But performance is not everything. As Pialis also noted, NVIDIA’s software platform including CUDA gives it a huge advantage in the AI chip segment. The ecosystem effects are strong, as engineers and researchers develop optimized frameworks and models for NVIDIA’s architecture. However, there is potential for alternatives; for example, . Pialis opined that a rule of thumb for AI hardware companies is that for every hardware engineer, they need 2-3 software engineers. So even though there’s a lot of talk about NVIDIA and hardware, the vast majority of the investment actually goes into the software. building upon AMD’s hardware and software stack https://www.youtube.com/watch?v=VYx1rGieDmE&embedable=true This was confirmed by NVIDIA’s Dave Salvator. At the , Salvator said that NVIDIA has twice as many software engineers as hardware engineers. That’s not at all by accident, as he emphasized. last MLPerf results briefing for 2023 However, Pialis sees far more potential for challengers in . The reason is that standards are still emerging in that area. As OctoML’s Luis Ceze , there’s for LLM serving, for portable deployment, and and for multiplexed fined-tuned model inference. Inference, is in fact, a bigger market than training as Pialis noted. the inference accelerator market shared vLLM MLC-LLM LoRAX Punica “There’s so much focus on training, large models and the cost of training. But it’s deployment where we all benefit from, on the inference side. That needs significant scale. That needs a different solution. More chips will inevitably be sold on the inference side than the training side. I’m sure business plans also improve with that increase in volume”, Pialis said. Startups like and are attracting significant funding, and so are companies from countries like the UK, Korea, and China trying to reduce reliance on US firms. As far as hyperscalers go, Pialis sees Amazon and Google as being neck to neck, with Microsoft showing strong momentum, and Meta being behind and rumored to acquire one of the smaller startups. Groq Tenstorrent Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives. Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. The Chiplet Technological Revolution One of the biggest transformations underway in semiconductors, according to Pialis, is the shift to . Previously, progress meant integrating more and more functionality into a single monolithic chip. But transistor dimensions are now down to ~5 atoms wide, and even tiny defects can ruin a whole chip. chiplets Pialis shared an anecdote from his own experience to highlight this. As he related, when he visited OpenAI at some point, he saw a bunch of engineers on their knees praying in front of servers. Not because they were “ ”, but rather because they didn’t want the model they were training to crash. feeling the AGI Some of us may have first heard about chiplets in the context of the China–US trade war that spills over to technology. Chiplets are a key element in the strategy of both and the . China US For Pialis, “chiplets are another technological revolution to deal with the problem that God has thrown at us.” That may sound like a wild appeal to the supernatural, but it has more to do with the challenges of physics at nanoscale. “When we build transistors, which are the basic building blocks of any integrated circuit, we’re stacking atoms. When you’re stacking atoms, the laws of probability, the laws of averaging, fall apart, because now it’s two atoms rather than hundreds of atoms and electrons. So what you get is defects”, Pialis explained. Chiplets are an innovative solution to this nanoscale physics challenge. Rather than one giant chip, designs are split into smaller Lego-like chiplets that can be connected through advanced packaging. This modularity lets chipmakers avoid throwing out entire devices due to one small flaw. The benefits, Pialis said, are important for both makers and buyers. “Silicon is no longer the foundation of leading edge semiconductors; it’s the package. Silicon is now something that is a component on this packaging. There’s been a lot of talk about semiconductor supply chains. There’s a lot of silicon capacity now. Where there’s still very little to no capacity is on the packaging, specifically for designs built using chiplets”, Pialis noted. Chiplets as Lego Building Blocks However, there is a standout example of an AI hardware company that seems to be successful, despite going the opposite way: Cerebras. Cerebras is known for its wafer-scale hardware. Pialis was positive that Cerebras was going to encounter the same defects as everyone else. The difference with Cerebras is they have redundancy. A wafer is like a panel, Pialis noted. Cerebras doesn’t even cut the panel. What they do is they have software that works around the defects. Fundamentally, they’re using silicon as the foundation rather than packaging. They have multiple chips that are arrayed, but they’re not cut. They just connect on the wafer. It definitely is a different approach. However, Pialis added, there are advantages to cutting things up. Imagine a vendor like Intel is trying to build different units such as a CPU, a GPU, a DPU, or a networking device. By breaking things up into smaller pieces, they become like Lego building blocks. So you can have a processor core chiplet, a PCI Express connectivity chiplet, an Ethernet networking chiplet, a DDR memory I/O chiplet, and a memory I/O chiplet. These chiplets can be mixed and matched in a package to build out an entire product portfolio. Pialis thinks that from a design complexity and upfront investment perspective, that’s a winning formula. Pialis sees massive benefits in chiplets, reducing costs by over 60% and power consumption by 40%. This is a major incentive for hyperscale data centers. While he sees Apple, AMD, and Intel leading in chiplets today, Pialis believes chiplets will soon become essential for any company focused on leading-edge hardware. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Software and Chiplet Modularity, Composability, Programmability Drawing parallels from the world of software engineering, one can wonder how come the modularity that chiplets bring to the table did not prevail earlier. But in hardware, up to now, the winners were the ones that could integrate into a monolithic device the most. The reason is that this drove down cost, which Pialis noted has created “a maniacal focus on integration.” However, as we now approach the dimensions of a single atom, the manufacturing cost eclipses the cost of integration. But even in software, . excessive modularity can also incur excessive overhead Pialis expects that some hardware vendors may take the chiplet approach too far. If functionality is broken into too many tiny pieces, the cost of integrating all of these becomes limiting. So ultimately, it’ll be a hybrid approach that wins. There are two ways of splitting things up with chiplets. The first way of building chiplets is building a single Lego block, mirroring it over and over with the same functionality, and they just talk to each other. That seems more akin to the “traditional” approach. However, one may wonder how these identical chiplet building blocks can be synthesized. The answer is, again, software. Different packages can be composed for different needs, based on the same chiplet, using the software. For example, multiples of 1, 2, 4, or 8 chiplets. The same silicon, just packaged differently, priced differently, and with different software to leverage the incremental compute and memory bandwidth associated with these devices. The other way of building chiplets is by splitting and cutting, creating Lego blocks for different types of functionality. This creates chiplets like a compute Lego block, a training I/O Lego block, a network I/O Lego block, and so on. Pialis sees more momentum behind this approach, as reusable Lego blocks can accelerate other products too. Analog AI, Optical Computing, and AI-Assisted Hardware Design Chiplets may be the immediate way forward. But at a time when power-hungry digital accelerators like GPUs dominate, Pialis claims there’s more to the future of AI chips. And that involves a radical break with the current paradigm. AI needs massively parallelized arithmetic processing, and binary is the dominant approach for this. Using binary compute, numbers are represented as ones and zeros, and floating point arithmetic is done defining precision and range. In analog arithmetic processing, floating point can be represented via voltages or currents. In theory, that may have infinite precision. In the real world where noise exists, that may yield less precision. But that may well work for applications. Using very small currents keeps things operating at low power. Edge AI https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true There’s another form of compute that some companies are investing in: optical compute for arithmetic. Optical compute uses the properties of optics to implement what are called (multiply & accumulate functionality) which is the core of any arithmetic unit. This results in even lower power demands. MACs Pialis noted that analog and optical approaches are attracting billions in investment to meet specialized needs around computational scale, energy efficiency, and precision. However, it remains unclear if analog techniques can scale effectively to match digital compute for cutting-edge AI models. There is active debate on this question in the hardware community. Even though these techniques are often dismissed as impractical, Pialis believes the investment will eventually pay off. For example, recently, it was reported that capable of processing computer vision tasks with remarkable speed and energy efficiency. researchers at Tsinghua University have developed the world’s first super all-analog photoelectric chip Finally, there’s also the question of . Pialis said that the most effective hardware designers these days are the best software developers. But if AI models can be trained by leveraging the wealth of their experience, that may bring about a sea change. using AI to help design hardware to power AI While the future of AI chips is unknown, Pialis believes the fundamental principles of engineering are timeless. Hopefully, that translates to something that does not end up draining the world’s energy and resources on the promise of future gains to be made.

What Is the Future of AI Chips? Leaders, Dark Horses, and Rising Stars

*** The History of Science Funding The scientist has always been a risky employee in terms of ROI.  They are trying to uncover truths about the universe, and oftentimes, the universe gives them the bird. Because experimenting means risking a lot of money and time on a possibly worthless idea, a scientist is a forever intern, until they make a breakthrough, then they become senior associates. That said, the scientist has been trying to make his own money. With the Alchemists, this was literal. While they invented Chemistry and catalyzed the birth of worldwide gold rushes, turning lead into gold was, alas, a wrong idea believed for centuries. The search consumed the lifetime of one person after another, with only dreams of wealth to get them through. But their tenacity was a testament to their resilience and patience. Patience meant, for the time being, remaining in the employ of someone who could afford the risk of backing science in the past. And that meant a King because they had gold/money collected from taxes. Wizards and fortune tellers didn’t make much sense, and they were comedians come to give the King a jolly time before he went to war. Though, while throwing guts on a wall may be stupid, it was mysterious and if they were right, doubly impressive. And here was this (broke) scientist, insisting that they knew a REAL strategy to help the King win. What was their credibility? Circa 200BC, the scientist is Archimedes and the King is the political head of Syracuse. Being a leader in those times largely meant you needed to win wars and as history had taught Kings the utter uselessness of charlatans, the scientist was given a chance to make his case. Archimedes made his case alright. It that he sunk the ships of the Romans with ingenious cranes and catapults that threw heavy stones. has been recounted Since his time, patronage from Kings, Princes, Bankers and Presidents to scientists has been immense. The scientist is respected, and all great scientists, even Leonardo da Vinci who was mostly fawned over for his impressive artwork, took off some time to design weapons. For weapons help the King win and hence, keep the scientist with a job. Scientists soon became ever more pivotal in war efforts. Especially Physicists and Chemists, who have designed countless forms of military hardware to date. However, the emergence of scientific methods in Economics, Sociology, Psychology, Biology, all riding on the power of statistical analysis and logical inference, quickly made the scientist important in every field of human endeavour. The problem however remained. To get a loan, the scientist usually had to, and still has to, rely on government funding because the government remains the biggest fish able to take on sufficient risk in the financial markets. Hate it all you want, but without credit from the government, be it debt or real money, a lot of scientific work would have stalled. Best example - SpaceX. However, governments and their ability to debase money and finance things on debt, made the world wars last longer to destroy the world much harder. It also led to economic depressions and after the 2008 financial crisis, Satoshi Nakamoto, a genius computer scientist, decided to create Bitcoin – a monetary tool outside the purview of government currency debasement. Birth of a financial model divorced from the King Bitcoin has been controversial from its inception. Of course, Kings and governments hate it. It is cutting into their business. A big part of the private sector also loathes its nature of priding in technological exclusivity, so that six years later, a hard fork was implemented to create Ethereum, a cryptocurrency built to provide extra blockchain services such as smart contracts. Many other hard forks quickly followed from the faithless. However, Bitcoin is still mainstream and has recently become interesting to Big Finance. The Bitcoin ETF proves as much. After 15 years of laughing at a thing that keeps building bigger and better, even the harshest critic will shut up and start listening more intimately. Because they now know they are missing something. Energy scientists are also starting to pay attention, with some claiming that Bitcoin mining may be our . are doing well selling cheaper electricity packages that have been subsidized by Bitcoin mining on the side. fast track to an energy transition Energy engineers But these are late to the party. Computer scientists have been earning well in Bitcoin, Ethereum, Doge for decades. And if any scientist has been comfortably living outside the need to get big budget funding from governments, that has been the computer scientist, from way back in the 20th century. While the field of Computer Science is much younger than Physics, Astronomy, Biology, many computer scientists quickly learned to earn big from private enterprise and individualized work, without begging the Kings and their Big Bankers for a loan. Futuristic Science Funding itself through Bitcoin Bitcoin is money for the people, and science has evolved to also be something done as a public good, on an open source plan, by dedicated people who put in their own time and money to make something good happen. As I mentioned, Computer Scientists now exist on the internet who are eagerly improving the Bitcoin ecosystem using the power of collaboration and their earnest efforts. These mostly come from the land of software development and after getting orange-pilled, decide to stay at Bitcoiner-land and build the Bitcoin ecosystem. However, there has not been much of an on-ramp for other types of scientists and this is the opportunity existing, wide open, for anybody to take. Currently, many a scientist in the Bitcoin world is a computer scientist of the type who builds zero-knowledge proofs, rollups, and more layers of the Bitcoin blockchain. But many more scientific people are needed. To re-iterate, I feel more electrical and power engineering scientists should be at the forefront of how Bitcoin mining equipment can provide electricity access to those without, making investments in clean, stranded energy sources much easier to manage. What better job than to be the electric engineer who designs their own job by bringing electricity to your people? Every university student of electrical engineering, whatever the developing country, should learn about this. Because developing countries in Africa, Asia and Latin America are struggling with scaling electricity usage from otherwise cheap energy sources, because of the high premiums that locals just cannot pay. The knowledge of using Bitcoin to subsidize electricity access from stranded energy sources should be spread as far and as widely as possible. Now, as an aspiring quantum information scientist, I feel there is also a place for me in the Bitcoin ecosystem. While the public might know how quantum information science threatens to undo the good that Bitcoin has done by potentially hacking it, I find that building quantum-resistant algorithms is far from scratching the surface of quantum capabilities. For example, quantum technologies could provide for Bitcoin nodes, allowing even weak signals to be successfully transmitted and ensure blocks are mined. better signal sensors Quantum computing could help guide better on how Bitcoin infrastructure could be better set up across nations and continents for optimum usage. solutions in logistics Quantum networks could provide better ways to securely. transmit Bitcoin information Some problems in the Bitcoin ecosystem could be (Bounded-error Quantum Polynomial) problems, which are the purview of quantum information science and these should be discovered and worked on using quantum computers. BQP Most importantly, quantum computers will be orders of magnitude better at mining proof-of-work cryptocurrencies than classical computers. When Bitcoin befriends Quantum Mechanics Moore’s law is coming to an end. IBM has already built transistors with only 5 atoms. To get smaller, it becomes harder to manage thermal noise and other nanoscale problems. To manage thermal noise, even more cooling will be done, the way it is done for qubits. Also, a 1-atom transistor is effectively a qubit. This is the quantum realm, and like it or not, more people will study quantum mechanics in the not-too-distant future, and by extension, quantum computing. The classical computing world can only scale by a multiplicity of the same technology, which will consume even more energy. The efficiency gains of quantum technologies like quantum computers will be needed. While , the science of building qubits is ramping upwards and systems are getting more powerful. There might not be an exponential growth in qubits with their own Moore’s law, but a linear growth in qubits leads to exponential computational power. Which kind of is a Moore’s law! I have said quantum computing is very expensive You remember this graph; If approximately 14 billion dollars in 2023 can bring in (logical qubits are the real qubits as manipulated in mathematical theory), by linear extrapolation, $125 billion by 2030 as seen above will bring in 428 logical qubits. 48 logical qubits But 7 years from now, Bitcoin itself might have ballooned to $200,000 per bitcoin which is a $4.2 trillion market cap. By linear extrapolation, $3 trillion is needed in global investments to take us to 10,000 logical qubits. As we can see, Bitcoin alone can pay for it over a 7-year span. Why 10,000 qubits? My Research Project Over the past three months, I have been studying QCourse551-1 which was offered in a collaboration between QWorld and Classiq Technologies. My research project was Project 11 - Bitcoin Mining using Grover’s Search Algorithm. The motivation for my project came from that shows that 10,000 logical qubits alone can do Bitcoin mining faster than any classical system using the same energy consumption. a research paper This is because 10,000 qubits can encode the full double SHA256 hashing process that goes into mining Bitcoin, but since they use Grover’s search algorithm, they effectively achieve quadratic speed-up by square_root(N) where N is the number of elements to check in an unstructured database. ASICs mining for bitcoins also check an unstructured database of random hashes for the nonce value. The question is, which one is faster? Most ASIC miners today are checking terahashes or For example checks 120TH/s. That is 120 million hashes every second. 1 million hashes every second. this miner Can a quantum computer beat that? Of course. square_root( 120 million) = only 10954 checks per second. A 1,095,400 % efficiency increase. And the more the competition, the more the quantum computer tears ahead in the race. The is rocketing upwards of 600 million million hashes per second. This is outputting enough heat to make climate scientists worried sick. But what if we replaced all those clunky ASICs with state-of-the-art QPUs from the year 2032, how many comparative hashes per second would they clock? square_root( 600 million million) = 24 million checks per second. A . current hash rate 2.4 billion % increase in efficiency If the above is not an attractive proposition, I don’t know what is. Bitcoin should fund quantum computing research. It will take a while, but when it happens, it will be glorious. It will be a more canonical event than the Moon landing. In our research, we were checking a much-simplified system with only 8-bit inputs and outputs, modeled on a Grover’s operator working with under 25 qubits. Given our really low hashing power, the advantages of Grover’s search could not be gleamed first hand, and could only be inferred with a relevant complexity analysis of the runs/depth of the Classical computer (my laptop) vs the depth of our Grover’s circuit executed on the Classiq Aer simulator. While it would have been good to model the energetics and the complexity graph of what this means, unfortunately, we ran out of time. However, we had some deliverables to show for our work. Below is what we had including the depth of each circuit. a) The simplified MD5 classical algorithm We modeled a simplified version of the MD5 (a most primitive cousin of SHA256) hashing 95 printable ASCII characters to find the nonce for hex values with difficulty = 1. Note: Our hex values were only 2-digit because we are working with 8-bit values. So our possible difficulty 1 values are only in 16 possibilities i.e. 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f. 00 is also a difficulty 2 element. Hence the searching/hashing function is called simplified_MD5_8bit_difficulty_1or2. def simplified_MD5_8bit_difficulty_1or2():
    printables_list = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
    for i in printables_list:
        x2 = ord(i)

        x3 = bin(x2)[2:]

        if len(str(x3))==7:
            x4 = x3+'0'
        if len(str(x3))==6:
            x4 = x3+'00'

        x5 = list(x4)

        a0 = 2*int((x5[0]))
        a1 = int(x5[1])

        b0 = 2*int((x5[2]))
        b1 = int(x5[3])

        c0 = 2*int((x5[4]))
        c1 = int(x5[5])

        d0 = 2*int((x5[6]))
        d1 = int(x5[7])

        a = a0 + a1

        b = b0 + b1

        c = c0 + c1

        d = d0 + d1
    
        a = ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b)
        
        print()

        d1 = d//2
        d0 = d%2
        d = d1*(2**7) + d0*(2**6)

        a1 = a//2
        a0 = a%2
        a = a1*(2**5) + a0*(2**4)

        b1 = b//2
        b0 = b%2
        b = b1*(2**3) + b0*(2**2)

        c1 = c//2
        c0 = c%2
        c = c1*(2**1) + c0*(2**0)

        decimal_result = d + a + b + c
        binary_result = bin(decimal_result)[2:]
        print(binary_result)
        hex_result = hex(decimal_result)
        print(hex_result)

        if hex_result == '0x0':
            difficulty_2.append(hex_result)
            collisions_difficulty_2.append(i)
        if hex_result == ('0x1' or '0x2' or '0x3' or '0x4' or '0x5' or '0x6' or '0x7' or '0x8' or '0x9' or '0xa' or '0xb' or '0xc' or '0xd' or '0xe' or '0xf'):
            difficulty_1.append(hex_result)
            collisions_difficulty_1.append(i)

    print('Difficulty_1 list = ',difficulty_1)
    print('Difficulty_2 list = ',difficulty_2)
    print('Colliding inputs for difficulty 1 = ',collisions_difficulty_1)
    print('Colliding inputs for difficulty 2 = ',collisions_difficulty_2)
    
difficulty_2 = []
difficulty_1 = []     
collisions_difficulty_1 = []
collisions_difficulty_2 = []

import numpy as np
simplified_MD5_8bit_difficulty_1or2() As can be seen, to find the difficulty 1 (and by extension the difficulty 2) elements, we have to reiterate through 40 lines of code (ignoring spaces and printing in the for loop). There are 96 printable ASCII characters to hash and check hence 96 * 40 which gives us a circuit depth of 3840. However, this is run through fast by my laptop. Less than a few seconds. b) The simplified MD5 quantum algorithm We modeled a quantum version of our simplified MD5 algorithm to simulate finding the difficulty 1 nonce values no matter what the input ASCII character to the circuit was. This is possible because all we need is a zero at the beginning of our hex value. Since our hash set was small (only 96 ASCII characters), we tuned the system to ignore the input ASCII value so it is redundant, and to aim to capture all possible zeros given the Arithmetic expression; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) which is also in the classical Python algorithm. Notice that the 4 functions being added together are derived from the . original MD5 algorithm Since we want difficulty 1 values, we add and to the above function and then synthesize it in the Classiq platform. d = 0 a = 0 (Note: This is a shortcut thanks to the tireless work of the Classiq team. The Python code was also built in the Classiq Python SDK but it had a last-minute glitch. Hence we only have working Python SDK code for the Grover’s operator, but not for the Grover’s operator with a difficulty-1 hash/search, which is what you had to see here ) Hence our input Arithmetic expression is; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) and d == 0 and    a == 0 The resulting circuit has a depth of only 913 The quantum circuit wins! Endnote While the project time ended, our work is still a work in progress, to continue until we build a functional 10,000 qubit quantum bitcoin miner. Voluntary efforts to support us technically are most welcome. Some of our code is still glitchy and we have only scratched the surface of the Classiq platform’s capabilities. An ingenious software tool that takes away the cumbersome tinkering with gates, as would have been needed for anybody building the same project using Qiskit. Special thanks to Classiq and QWorld for the opportunity. And our mentor - Dr. Eyal Cornfeld, for the guidance. ***

Hash Rate Analysis Projections: Why Bitcoin Should Embrace Quantum Information Science

হ্যাশ রেট বিশ্লেষণ অনুমান: কেন বিটকয়েন কোয়ান্টাম তথ্য বিজ্ঞানকে আলিঙ্গন করা উচিত

Prognosen zur Hash-Rate-Analyse: Warum Bitcoin die Quanteninformationswissenschaft nutzen sollte

Proyecciones del análisis de la tasa de hash: por qué Bitcoin debería adoptar la ciencia de la información cuántica

Projections d'analyse du taux de hachage : pourquoi Bitcoin devrait adopter la science de l'information quantique

हैश दर विश्लेषण अनुमान: बिटकॉइन को क्वांटम सूचना विज्ञान को क्यों अपनाना चाहिए

ハッシュレート分析の予測: ビットコインが量子情報科学を採用すべき理由

해시레이트 분석 예측: 비트코인이 양자 정보 과학을 수용해야 하는 이유

Projeções de análise de taxa de hash: por que o Bitcoin deveria adotar a ciência da informação quântica

Прогнозы анализа скорости хэширования: почему Биткойн должен использовать квантовую информатику

Hash Rate Analizi Projeksiyonları: Bitcoin Neden Kuantum Bilgi Bilimini Kucaklamalı?

Dự đoán phân tích tỷ lệ băm: Tại sao Bitcoin nên áp dụng khoa học thông tin lượng tử

哈希率分析预测：为什么比特币应该拥抱量子信息科学

Dr. Sabine Hossenfelder’s is titled ‘ ’. latest YouTube video It looks like AI will kill Quantum Computing I would like to share why this shall not happen before this jab at quantum computers turns cold. Quantum Computers Are of Great Research Interest If you went away from the video with an earnest belief that AI shall indeed kill quantum computers by drying up its research funding, then you might as well believe that AI will kill particle physics, cosmology, and quantum gravity. And where the hell is the Higgs Boson hiding if it holds more mass-energy than a hydrogen atom? Just saying. Continuous progress in the theoretical and experimental realization of better quantum computers is of great interest to human beings because it is a real way to uncover computational truths about the universe. We are curious about building big, powerful quantum computers like we are curious about what happens inside a black hole. Shall a big powerful quantum computer hack RSA and uncover all those hashed-up secrets (about aliens)? I want to find out. Does a black hole teleport us into another Universe? Dr. Sabine is interested in this one. As AI makes big tech founders billions and soon to be trillions of dollars once we start putting servers on the moon, a lot of that newfound funding will go towards, take a guess, research in quantum computing. Computing Is Not Always About Speed The Graph We are always comparing the computational speed of quantum computers with that of classical computers in the graph below. The graph above starts with the line for quantum computing above that of classical computing. This means that classical computers are more efficient and take less time to do operations. Later on, there is a cross-over point, and classical computers take way more time to do the operations compared to a quantum computer. That cross-over point is when a quantum computer of modest size and complexity hacks classical encryption algorithms, simulation problems, and that no classical computers can hack. traveling problems Dr. Sabine argues that AI is shifting the lines and the cross-over point into the very far future, making Meta senior fellow and former tech chief Mike Schroepfer exactly right when he says “quantum computing being irrelevant to Meta right now”, quite prescient. …”(quantum machines) may come at some point, but it’s got such a long time horizon that it’s irrelevant to what we’re doing.” source But computing is not always about solving problems quickly. Modelling Computing is also about modeling problems. While the latest QC - the - is not gargantuan enough for the computational problems that most people are paying for, the fact that you would need a supercomputing complex to model what it can model should be impressive enough. 433 qubit IBM Osprey Know what, we should do it. Let us model something impressive in Osprey and also try to model it on See which one uses less electricity. This modeling contest can be appreciated as high art and designed to get people entertained. Where there is entertainment, people are willing to pay for it. Frontier. On a more practical note, quantum sensors built at the measurement side of quantum computers are a peak into the universe that classical computational sensors have no hope of seeing. Of note are LIGO and Atomic clocks. The latter can see and count atomic zeptoseconds using minimum energy periodically cycled by continuously exciting an atom. Telling time very accurately helps us keep an accurate, albeit . clunky calendar The former can see gravitational waves using a variation of the Mach-Zehnder interferometer. In the quantum computing world, this is just a singular CNOT gate. So, if one CNOT gate can see gravitational waves, wonder what our elaborate algorithms can see. We need to find out. The measurement-computing use cases for quantum computing shall only get more impressive. I hope to one day measure the Hamiltonian of the . smallest Bitcoin miner possible AI Might Love Quantum Computing We need to realize that AI might love quantum computing, because AI loves learning. AI might be pushing the boundaries of classical computers to make them develop heuristic powers that people who train AI models also marvel over. But no matter how hard you push a car, it will never be a boat. While AI is still poor at explaining quantum mechanics compared to most human physicists, that might change once we keep pumping AI with quantum computing data. Machine Learning of Quantum (MLQ, not which is ) technology should be of interest to Meta because the road to developing better quantum computers will also develop better nanomaterials relevant to the classical computing world. QML Quantum Machine Learning The quantum computing world, like the classical computing world, is now parked at the nanoscale. Being neighbors, they could benefit from each other. They will. P.S. >> Quantum Computers also have their finger on improving battery technology because an entanglement of atoms packs more energy than if you did not have an entanglement. Admit it; these quantum computers are interesting.

AI Will Not Kill Quantum Computing

এআই কোয়ান্টাম কম্পিউটিংকে হত্যা করবে না

KI wird Quantencomputing nicht töten

La IA no acabará con la computación cuántica

L’IA ne tuera pas l’informatique quantique

एआई क्वांटम कंप्यूटिंग को खत्म नहीं करेगा

AI は量子コンピューティングを破壊しない

AI는 양자 컴퓨팅을 죽이지 않을 것이다

A IA não acabará com a computação quântica

ИИ не убьет квантовые вычисления

Yapay Zeka Kuantum Bilişimi Öldürmeyecek

AI sẽ không giết chết máy tính lượng tử

人工智能不会杀死量子计算

Social media has become deeply embedded into the fabric of society, enabling global connection while also consolidating power in the hands of centralized platforms. However, emerging models built on blockchain technology offer an alternate vision that could realign social media with its original empowering promise. web3 This examination will analyze the landscape of both centralized and decentralized social platforms. It will explore how core components like cryptography, blockchain ledgers, tokens, and DAOs could provide the technological backbone for social networks owned and operated by the users themselves rather than corporate gatekeepers. While this transition faces profound technical and adoption hurdles, web3 presents an opportunity to remedy the increasingly evident harms of consolidated control - from data exploitation to algorithmic manipulation. Realizing that potential will require surmounting complex challenges around governance, incentives, and usability. However, the momentum toward decentralized models is accelerating as scandals mount. The Troubling Trajectory of Centralized Social Networks Before envisioning how decentralized protocols could transform social interactions online, it is important to trace the origins of today's dominant model of centralized control. In the early days of social media, visionary pioneers saw opportunities to connect people around the globe in profoundly democratizing ways. Platforms like Facebook and Twitter offered tools for open sharing, grassroots organizing, and unprecedented access to perspectives outside one's geographical limitations. But as networks grew rapidly in scale and influence, the idealistic early social contract eroded. In response to market pressures and incentives, centralized platforms shifted toward data collection, targeted advertising, algorithmic curation, and other techniques for maximize engagement. While these tools increased profits and usage metrics, they also imposed mounting costs: - Comprehensive monitoring of activities, behaviors, and connections enables privacy intrusions and hyper-targeted advertising. Users have little visibility or control. Pervasive Tracking and Exploitation of User Data - Algorithmic timelines based on optimization metrics displace chronological transparency. This risks trapping users in filter bubbles. Curated Feeds that Maximize Engagement - Users generate content, networks, and value while platforms extract data for immense corporate profit. Relationships grow transactional. Asymmetries Between Users and Platforms - Consolidated content moderation policies increase efficiency but enable censorship and overreach. Some abusive behaviors still fall through the cracks. Risks of Overreach in Centralized Moderation - Central repositories of user data increase risks from hacking, data breaches, and state surveillance. Security Vulnerabilities from Data Consolidation - With decision-making concentrated in corporate bureaucracies, users have a minimal voice around governance, policies, or values. Limited Opportunities for Recourse This trajectory reveals how centralized control, while efficient, can distance platforms from the people and communities they were built to serve. Each wave of scandals and controversies illuminates the gap between Silicon Valley incentives and user interests. The Pillars Underlying Web 3.0 Models Growing disillusionment with centralized platforms provides fertile ground for decentralized alternatives. But transitioning from vision to reality requires building on three foundational pillars: - Rather than centrally managed databases, public blockchains rely on peer-to-peer networks applying transparent consensus rules to synchronize and verify data. Decentralized Blockchains - To incentivize behaviors that align with decentralized community goals, native crypto assets like Bitcoin and Ethereum's Ether enable the exchange of value and governance rights without centralized issuers or clearinghouses. Cryptocurrencies and Tokens - Smart contracts allow coordinating workflows, voting, treasuries, and resource pooling in a distributed manner according to embedded rules rather than top-down management. DAOs (Decentralized Autonomous Organizations) Together, these technologies enable platforms and communities coordinated by engaged users according to collectively agreed-upon rules and incentives rather than corporate intermediaries. Their application to social media re-opens profound questions about governance, economics, privacy, and speech online. Early Experiments in User-Owned Social Networks A new generation of blockchain-based networks offers glimpses into the possibilities of decentralized, user-driven social media: - An early leveraging the Steem blockchain to reward users, content creators, and curators with native STEEM tokens based on community votes. Steemit web3 social network - Uses crypto token models to incentivize ethical content creation, crowdfunding, and moderation on its open-source network controlled by users rather than corporations. Minds - Developing a decentralized identity layer for social interactions without surveillance or centralized data exploitation, along with participatory economics. Ind.city - An initiative by Twitter founder Jack Dorsey, exploring open recommendation algorithms to reduce centralized control over content visibility. Bluesky - Provides developer-friendly infrastructure for decentralized social graphs anchored to user blockchain wallets using public profiles and follows. Lens Protocol These experiments highlight paths toward social ecosystems aligned with user ownership, agency, and value creation. Decentralizing Content Moderation and Governance A key question facing decentralized social networks involves governance. Can community health be maintained without recourse to centralized moderation? Hybrid approaches blending blockchain transparency with user self-governance could enhance accountability: - Content rules and community guidelines get translated into inspectable and updatable software logic. Encoding Policies in Smart Contracts - Swarms of crypto-rewarded users help flag abusive posts and problematic accounts for collective review. Leveraging Token-Incentivized Validation - A weighted voting process considers both numbers of votes and intensity of preferences on contested moderation, limiting echo chambers. Quadratic Voting on Disputed Cases - Community treasuries, administrator keys, governance processes, and policies are directed by inclusive on-chain voting. DAOs Managing Shared Resources - Unique user IDs allow accountability and reputation portability without revealing personal details or enabling tracking across contexts. Partially Anonymized Profiles - On-chain records of content takedowns or account suspensions provide visibility into collective decisions over time, enabling oversight. Transparent Public Moderation Logs Deliberate crypto-economic designs could enable community self-governance at scale, with disputes resolved through collective wisdom rather than centralized authority. Restoring User Agency through Data Portability Centralized data silos impose steep costs in terms of privacy, transparency, and control. But users can regain agency within : web3 models - Users control access to profile data, relationships, posts, and assets via private keys unmediated by external platforms. Self-Sovereign Digital Identity - Granular visibility settings allow compartmentalizing personas and activities across apps to share only what's relevant with certain groups. Selective and Contextual Data Exposure - Following open standards, decentralized identifiers allow reputations, content, and networks to port fluidly across compatible platforms rather than remain siloed. Interoperable Social Graphs - Curated algorithmic timelines get replaced by interest-based social graphs modulated according to user choices rather than corporate optimization goals. User-Driven Interest Feeds By directly owning and controlling profiles, content, and social graphs via cryptography, users can dictate the terms of interoperability and portability. Their social capital gets decoupled from walled gardens. Aligning Crypto Token Models with User Value Carefully engineered crypto token models allow aligning economic incentives with user-generated value: - Direct micropayments in utility tokens build sustainable funding models where wide audiences multiply earnings. Users tip to access ad-free content. Compensating Creators through Tips - The visibility superpowers of likes, hashtags, and trends get modulated based on skin in the game. This resists bot manipulation. Curation Influence Proportional to Stake - Staking tokens allow accessing exclusive permissions, identity verification, specialized roles, higher visibility, or private communities. Frictionless Access Control - A portion of revenues flows into a community treasury governed on-chain. Users collectively decide on grants, incentives, or public goods. DAO Treasury Funds Ecosystem Growth - If incorporating supplemental ads or transaction fees, DAO governance allocates revenue shares to avoid concentrated corporate gain. Equity in Platform Monetization With carefully calibrated token engineering, blockchains enable direct economic alignment between users, contributors, and stewards supporting the social ecosystem. Micropayments as an Alternative to Advertising In addition to models, the microtransaction capabilities of blockchain systems offer additional alternatives to advertising: crypto asset - Cryptocurrency micropayments allow frictionless transactions of just pennies, unlocking crowdfunding and subscriptions at the nanoscale. Lower Barriers to Direct Exchange - Micropayments allow users to directly support creators and communities rather than having their attention treated as a product to indirectly monetize. Users Can Fund the Experiences They Value - Token-gated access also enables creators to convert engaged followings into exclusive, intimate social spaces funded directly by members. Creators Can Build Niche Audiences - Requiring micropayments can limit bots and trolls while funding moderation and governance. Vetting Mechanisms to Deter Abuse Direct exchange creates a social media economy centered on aligned incentives between engaged communities rather than centralized data brokerage. Obstacles Along the Path to Mainstream Decentralized Adoption Despite the conceptual appeal, migrating today's massively scaled networks into web3 models involves profound challenges: - Managing private keys, crypto assets, governance rights, and modular digital identities may feel burdensome to users habituated to centralized convenience. Mainstream User Experience Hurdles - Transitioning existing social graphs and accrued capital to new platforms faces extreme inertia, even when aligned with user interests long-term. Acute Platform Lock-In Effects - Current blockchain infrastructure remains inadequate for supporting seamless mainstream social interactions due to limitations in scalability, latency, crypto UI/UX, and standards. Technology Maturation Needed - As networks grow, the tendency toward consolidation and profit-seeking behaviors will require vigilant governance to resist. Ongoing Centralization Risks - While pseudonymity provides some protection, risks around stalking, misinformation, predation, and harassment may require diligent peer moderation. Safety and Privacy Tradeoffs Overcoming these challenges will require profound advances across technology, design, business models, policy, and community governance. Aligning Decentralized Social Networks with Human Values If decentralized models gain traction, policymakers will need to grapple with questions about rules, rights, and responsibilities in this new reality: What should be the balance between privacy, anonymity, transparency, and accountability? How do we embed ethics around justice, empowerment, compassion, and inclusion into community guidelines? Can protections against harms like misinformation be enforced algorithmically or require discretion? If web3 social networks gain economic influence, how do they ensure equitable access to participation? What kind of oversight roles should government regulators play within decentralized environments they don't directly control? The answers will shape whether decentralized social networks empower humanity's best or worst tendencies. Above all, these questions illuminate the need for humane technologists, policymakers, and communities to unite around shared values that uplift society. Conclusion As scandals and loss of public trust mount, centralized giants seem increasingly disconnected from their communities and users. But emerging web3 models offer hope of realigning social technology with its highest purpose - bringing people together. There are no shortcuts along this path. It will require time, experimentation, and multidisciplinary collaboration between technologists, creatives, policymakers, and community stewards. But the seeds of a more user-driven , free from exploitation and centralized agendas, have been planted. With patient nurturing, those seeds could blossom into social ecosystems where engaged users collectively hold power and ownership. social media The dominant platforms of today will evolve - or risk irrelevance. As user awareness grows, decentralized alternatives will only gain momentum. This historic transition will encounter challenges but also opportunities to learn from past mistakes. Most importantly, it’s a chance to recenter human needs - for connection, community, security, and belonging - in the digital public squares of the 21st century.

How Web3 Can Empower Users and Communities in Social Media

Internet of Bio-Nano Things (IoBNT) is a domain where biochemical processes inside the human body communicate the cyber world of the internet. IoBNT paradigm stems from synthetic biology and nanotechnology tools and enables the engineering of biological embedded computing devices called . nano-machines Nano-machines are powerful and functional man-made tiny devices. The functionality of these devices is inspired by the behavior of atomic and molecular structures composed of nanoscale components. They not only function as computers but also establish connections with the environment (human body) to detect a physical quantity, as living organisms. Communication inside the human body is as old as the existence of mankind. Indeed, the human body is a large-scale heterogeneous communication network of nano-networks as it is composed of billions of interacting nano-machines, i.e., cells, whose functionalities primarily depend on nanoscale molecular communications. Hence, the vital conditions of the human body directly depend on the performance, reliability, and continuous functioning of intra-body molecular nano-networks. Architecture of Internet of Bio-Nano Things Internet of Bio-Nano Things (Image Source: IEEE Communications Magazine ) IoBNT architecture is an extended version of the Internet of Things paradigm. IoBNT uses the cyber communication backbone of IoT. The main difference is in the perception layer, where the sensors are deployed. The sensors used in IoBNT are nano-scale and use the nano-scale communication technologies (Molecular Communication/ Terahertz based Nano electromagnetic Communication). Following are the entities involved in IoBNT architecture: Bio-Nano Things Bio-Nano Things are biological computing machines that collect sensory data from the environment (inside the human body) and pass it to the bio cyber interface. Bio nano things are used interchangeably with nanomachines. In order to develop efficient and novel nano-machines and to understand the communication mechanism between nano-machine, a study of biological cell architecture and their interactions has been proved helpful. Bio nano things are deployed in the form of inside the human body for biomedical applications. Below are five components of nanomachines. nanonetworks Image Source: FUTURE NOW THE IFTF BLOG It contains the embedded software, which aims to perform the intended task of nanomachine. The control unit controls all the other components and could have a storage unit. The nucleus of a biological cell is responsible for realizing its intended tasks. Similar to software conditional expressions biological control unit encodes protein structures, data units, and regulatory sequences. 1. Control Unit: The communication mechanism of nanomachine is realized through transceivers. Transceivers allow the embedded system to exchange information by transmitting and receiving messages at the nano level. The inter-cellular communication is realized through the gap junctions, hormonal and pheromonal receptors placed on the membrane of the cell. 2.Communication Unit: It contains the instructions to fabricate the components of nano-machines and then to replicate them. This process takes place when nanomachines are replicated by saving the code of nanomachines in molecular sequences. 3.Reproduction unit: Power unit supplies stored energy to all the other components of nano-machines, to maintain the electrical current in embedded software. Mitochondrion, chloroplast, and Adenosien Triphosphate are some of the substances of cells that correspond to the external chemical reactions to produce energy. This chemical energy is stored in the cell reservoirs and supplied to regulate the other components of the cell. 4.Power Unit: This unit provides an interface between the environment and nanomachine. The sensing and actuation is the ability of a biological cell to distinguish external molecules or stimuli e-g chloroplast of plants and flagellum of bacteria. 5.Sensor and Actuators: Bio cyber Interface Electronic tattoo can be used as bio cyber interface to communicate with in body biological nanonetwork(Image Source: IEEE Communications Magazine ) Biocyber interface is a specialized micro device that is usually implanted on the outer parts of the body. Bio cyber Interface acts as the transduction unit, which translates biochemical signals from the human body into electrical signals and electrical commands coming from healthcare providers into biochemical signals for in-body communication. Bio cyber interface has wireless communication capability which is used to communicate with gateway devices. The communication frequency of bio cyber interface is kept weak on purpose so that the high-frequency electromagnetic waves do not interfere with normal body functionality. Gateway devices Image Source: Medical News Today The gateway devices in IoBNT are usually a smartphone, tablets, PDAs or other wireless communication enabled handheld devices. Bio cyber interface communicates with gateway devices to send data and receive commands from the remote healthcare provider. Internet access point The connectivity mechanisms to forward and receive messages from healthcare provider/medical server. For example WiFi, 4G/5G, or other wireless communication mechanisms. Medical Server Photo by National Cancer Institue on Unsplash It can be a cloud-based service for storing and processing health-related data of patients and a web portal used by healthcare providers to analyze reports and consign treatment Communication among Nano-machines Nano-machines are only able to perform trivial tasks on their own; thus communication among nano-machines is very important to realize more complex tasks . Nano-machine communication technologies are divided into four groups namely: Nanoscale Electromagnetic communication Nanoscale Acoustic Communication Nanoscale  Mechanical Communication Molecular Communication Nanoscale Electromagnetic Communication This type of communication is based on the transmission and reception of electromagnetic waves between novel nano materials such as carbon nanotubes and graphene based nanoribbons. The traditional transceiver of classical wireless communication is not feasible for nano-scale communication, however novel graphene based nano-materials have shown potential to overcome this limitation.Future are envisioned to operate on terahertz band, while few terahertz channel exist to date. Electromagnetic communication can make communication possible from micro device to nano device, and research is being made for the communication between nano devices or from nano to micro device. electromagnetic nanonetworks Nanoscale Acoustic Communication Acoustic communication is realized by the transmission of ultrasonic waves through nano machine integrated transducers .These transducers should be capable to sense the variety of pressure and then react accordingly. Currently the size of transducers is the major barrier to implement this communication mechanism at nano-scale. Nanoscale Mechanical Communication In nano mechanical communication, the information is sent through nano machines that are linked physically. One of the major drawbacks for this communication technique in nano communication context is physical connection between devices. Therefore it is not workable for the applications where nano-machines have to be placed at distant locations. Molecular Communication Image source: IEEE Xplore Molecular Communication (MC) is a molecule based communication paradigm that enables transmission of bio-chemical information (e.g. status of living organisms), which is not feasible using traditional communication. Molecules encoded with information to be transmitted, are called information molecules. The information molecules activate bio-chemical reaction at receiver and may recreate phenomena and/or chemical status, which sender then transmits. Molecular communication (MC) is considered the most promising nano networking mechanism due to its nano-sized transceivers that can easily integrate into nano machine. MC is bio-inspired and closely relates to the communication phenomena existent in nature for years.MC is also paramount choice for bio medical applications that involve intra body communication. Different schemes for molecular communication can be categorized into short range communication using calcium signaling, medium range signaling using molecular motors and long range signaling using pheromones. Security for the Internet of Bio-Nano Things Nano networking is a novelty in the IoBNT domain. Thus, here we focus on the security requirements in nanonetworking only. The has already been discussed extensively. cybersecurity for IoT Global security goals for the classic communication paradigm are CIA (Confidentiality, Integrity, and Availability). These classic security goals will remain the same even in the context of the nano communication paradigm. For more holistic security requirements we have added ‘Authenticity’ as the fourth security requirement. Global Security Goals Confidentiality It is the most important issue in network security. This goal ensures that the context of the message exchanged between sender and receiver should not be accessible to an unauthorized entity. Encryption techniques can be used to add confidentiality to the communication. Integrity It assures that the received information is complete and correct i.e., without being modified by an external entity. To ensure integrity in communication MAC (Message Authentication Code) hash functions can be used. Availability It assures that information is always available; the attacker should not be able to disrupt communication at any time. Setting redundancy to the network can assure availability. Authenticity It ensures that the source of message transmission is reliable and stops the attacker from sending fake messages. Types of Attacks and Threats for IoBNT Identification of attacks and threats is also important for network security. The attacks can be performed by two types of attackers; Internal attackers and External attackers. are part of the system and have access to credentials and other information required to communicate with other system entities. Internal attackers in the scenario of nano communication can be divided into two types: Local and remote attackers. Local attackers are located in the vicinity of attacked nanosystem. Attacks like eavesdropping and spoofing can be performed by these attackers. Remote attackers have to become local attackers before launching the attack. External attackers The types of attacks launched by these attackers are classified into four groups: These types of threats include access to the system by unauthorized users. In the case of nano-networks, these types of attacks are complex to launch when using mechanical or molecular communication. However electromagnetic and acoustical communication opens the door for the attackers as the covered is area is relatively large. Disclosure: Attackers can falsify or manipulate data in this type of attack. Injecting false information in the network can challenge the system reliability. Integrity checks can be performed to limit these attacks. However computational protection schemes Deception: The availability and reliability can be stalled by this kind of attack. Given in the context of nano-networks the attacker can modify the parameters such as temperature and pH level to disrupt communication. Disruption: Unauthorized entities can control system services or entities, beyond just disrupting the system. This attack can enable attackers to cause malfunctions or even take over the entire system. Usurpation: Other types of attacks adopted from traditional networks are listed below: As the name suggests the attacker snoops the communication between two nodes. Eavesdropping: Attackers try to become trustable by spoofing other nodes and broadcast malicious information. Spoofing/Altering/Replaying/Injection: Forwarding of packets to nodes that are not intended for that message in form of a loop. Loops: Dropping some of the packets that must be forwarded to the next hop of deciding whether to forward or not. Selective Forwarding: Type of selective forwarding where packets are dropped. Black Hole: Corrupted node creates an artificial sinkhole by attracting as much traffic as possible. Sink Hole: The routing is disturbed by forwarding messages far way in the network through a secret channel. Warm Hole: Geographical routing protocols are disturbed by a malicious node claiming multiple identities. Sybil Attacks: Availability and energy of potential nodes are overtaken by corrupted nodes through sending fake messages. Flooding attack: : Sequence numbers of messages sent, are manipulated by the malicious nodes. Desynchronization attack : The attacker disrupts communication between other nodes by creating noise in the communication channel. Jamming attack Nodes are captured to get cryptography keys and sensors can also be reprogrammed to make them malicious. Node Capture: Bio-Inspired Approaches as Security measures for the Internet of Bio-Nano Things Existing mechanisms of security and privacy used for traditional data communication networks cannot be applied directly to the nano communication network paradigm. The existing cryptography and encryption techniques such as AES and RSA demand high computational power whereas nano-machines can only handle light-weight security solutions due to their nano-scale capacity. Using classical cryptography might be inefficient if only limited information is transmitted (like sending a small specific molecule to send one bit of information). Then adding a digital signature or long cryptography message authentication code is not appropriate. Many secure localization existing schemes are based on time-of-flight measurements; these are not directly applicable as they would require sub-nanosecond accuracy. However, there exist a number of bio-inspired algorithms for security that can be modified or down-scaled to the nanoscale level to make them feasible for nano-networks. Molecular Communication (MC) is one of the communication mechanisms of nano networking that uses molecules for information exchange. MC is itself bio-inspired and adapts the communication mechanisms existent in nature e-g communication inside the human body. There is an established research area that aims at using Bio-inspired approaches to solve problems according to the application domain. There are three research areas of bio-inspired approaches: Bio-inspired computing represents a class of solutions that focus on providing efficient computing solutions e-g optimization algorithms and pattern recognition. Bio-inspired systems class provides nature-inspired solutions for massively distributed and collaborative systems. Bio-inspired networking class provides strategies for efficient and scalable networking under uncertain conditions. There exists a classification of bio-inspired approaches that provides efficient solutions for security to the computer networks. Artificial Immune System (AIS) The artificial immune system has been defined by as “adaptive systems, inspired by theoretical immunology and observed immune functions, principals and models, which are applied to problem-solving”. There are a number of appealing features that make an immune system, whether it is a natural or artificial suitable candidate for securing nano applications using molecular communication. Those features are robustness, reinforcement, memory, distributed, adaptive, recognition and dynamically changing coverage. Castro and Timmis The main three research fields related to AIS are (1) Immune networks (2) Clonal Selection (3) Negative Selection. Swarm Molecular Security Approaches The concept of the swarm is referred to a grouping of individual units to accomplish complex tasks that are difficult to be realized alone. Swarm based security approaches are highly potential for nano-networks security as they can be realized in lightweight systems. This type of security approach is highly effective since they are robust, self-configurable, and adaptive. The mechanisms adopted in nature to defend against attacks and intruders can be treated with . Similarly, nanorobots have minimal intelligence and capabilities individually, but when they form a group, they can provide an effective mechanism against intruders. swarm intelligence Bio-Chemical cryptography have defined biochemical cryptography as “a primitive that may be used for efficiently securing biologically based information channels.” The biochemical cryptography techniques proposed so far use DNA molecules for information encryption. Falko Dressler et al Significance of Security on the Internet of Bio-Nano Things By connecting the human body to the outside cyber world through IoBNT poses a number of threats and security issues to the human body. There are a number of possible threats and attacks that can be launched to disrupt communication. Ian Akyildiz and his group members have coined the term for IoBNT attacks in their pioneering work on IoBNT.Security mechanisms used for traditional networks demand high computation power which is beyond the capabilities of nano-machines. The available memory and processing capabilities of nano-machines are extremely limited, which makes use of complex communication algorithms and protocols impractical in nano regime. There is a huge asymmetry between the computational power of desktop computers and a single nano-machine. This limitation might affect the achievable security level, as one might have to work for short key lengths due to resource constraints which would allow attackers to easily perform brute-force attacks using high-performance computing e-g available through graphic cards. Bio-Inspired networking is an established research area that provides solutions, adapted from nature to a number of computing problems. IoBNT has many potential health applications which facilitate patients to relieve from lengthy medical tests, sparing time for appointments from health expert and necessity of being on the same location as that of health provider. "Bio Cyber terrorism" ) (Featured Image Source: IEEE Globecom

WTF is Internet Of Bio Nano Things (IoBNT) and How Secure Is It?

Nanotechnology is defined in a number of ways. One such definition is provided by which states nanotechnology as “a branch of technology dealing with the manufacture of objects with dimensions of less than 100 nanometers and the manipulation of individual molecules and atoms”. Collins English Dictionary. Copyright © HarperCollins Publishers Nanotechnology deals with the miniaturization and fabrication of devices on a scale ranging from 1 to 100 nanometers. The prefix nano means one-billionth i.e., (1x10-9). was the physicist who discussed the possibility of dealing with molecules directly in his lecture "There's Plenty of Room at the Bottom: An Invitation to Enter a New Field of Physics" at the annual meeting at Caltech on December 29, 1959 Richard Feynman American Physical Society Nanotechnology has a number of applications in multidisciplinary fields such as physics, chemistry, biology, sports, health, computer science, and ICT(Information and Communication Technology) to name a few. This article explores the applications of nanotechnology in the fields of computer science and ICT. Fabrication of nano-sized devices called is made possible through nanotechnology tools. Nanomachines are functional devices and capable of performing trivial tasks like sensing, actuation, computing, and data storage. Single nanomachines are only capable of performing trivial tasks. nanomachines Therefore, to perform more complex tasks they must be interconnected to form a network. Nanomachines can be manufactured using three approaches top-down, bottom-up, and bio-hybrid. Approaches to manufacture nanomachines Image Source: Stanford Online In this method atoms and molecules are self-assembled into nano- structures. Hence bottom-up refers to the construction of large objects from smaller building blocks, i.e. construction of nanostructures using atoms and molecules, rather than bulk materials. Bottom-up: Top-down approach is the building of nanostructures and materials by mechanical methods such as lithography, and by bulk technology. The top-down method for processing of nanostructured materials involves starting with a bulk solid and then obtaining a nano-structure by its structural breakdown. Mechanical and thermal methods are also used for the preparation of nanostructured materials from the bulk. Top-down approach: Bio-Hybrid approach is the assembly of nano-sized biological components and manmade nano-structures, to develop new nano-machines. This process includes positioning of the constituent sub-nano building blocks into an assembly of a functional structure. This approach is very expensive and time-consuming as compared to other states of the art of manufacturing technologies. Bio-Hybrid Approach: Molecular Communication: A nano networking technology Image Source: Foundry4 Nano communication network is a novel communication paradigm that enables communication among nanoscale devices at a scale ranging from 1-100 nm. At this scale, the receivers and senders used by traditional wired and wireless communication are not workable. Therefore nanomachines have been developed to realize communication at this tiny scale. Nanomachines are tiny devices that are able to perform trivial tasks and can do complex tasks after forming a network (nano network). Nanomachines can be biological entities or can be synthesized materials. is the paradigm in that utilize molecules for communication among nanomachines. Molecular communication is biologically inspired i.e., it adapts the communication mechanisms already existent in nature for communication among living organisms. Molecular communication (MC) nano communication networks The human body is composed of a large-scale heterogeneous network where molecular communication takes place for intrabody communication. There are many intrabody applications where small-scale communication is necessary e-g targeted drug delivery, BMI (Brain Imaging Interface), tissue engineering and cell repair etc. Various communication and networking aspects of MC are currently being explored by the research community. Molecular communication is as old as the existence of nature, as communication has been taking place between living organisms since then. However, with the advancement in computer networking, the significance of Molecular communication has been brought to light all over. The research on molecular communication from the networking perspective is almost two decades old but still is immature. There are still a number of open issues regarding the design and mathematical modeling of system components that need to be addressed. Communication ranges of Molecular Communication Molecular communication is able to take place in three ranges. (1) Short-range communication using molecular motors is the mechanism where inter-cell and intra cell communication takes place using molecular motors, which are carriers of information encoded molecule. (2) Short-range communication using calcium ions is another mechanism of molecular communication where communication might take place either between physically adjacent cells or distant cells using calcium ions (Ca2+). (3) Long-range communication using pheromones is the communication mechanism that takes place between sender and receiver nanomachines that might be millimeters to kilometers apart. Application domains long-range communication is military field and environmental applications. Molecular Communication reference models In this section, some prominent architectures and conceptual models for MC are explored. We know that the is the conceptual communication model used for traditional wired and wireless networking systems. Researchers in MC have proposed a similar layered model for MC as well. OSI ( Open Systems Interconnection) model consists of five layers i.e., Application Layer, Molecular transport layer, Molecular network layer, Molecular link layer, Signaling sublayer, and Bio-nanomachine sublayer. have described the functionality of each layer in detail and highlight the open research challenges according to each layer. The molecular communication Nakano et al TCP is a connection-oriented protocol used in traditional communication. communication protocol for molecular communication between biological nanomachines is also proposed, to avoid congestion between sender and receiver. TCP-like connection-oriented has been explored extensively. Intra-body molecular communication channels, i.e., nanoscale neuro-spike communication channel, action potential-based cardiomyocyte molecular communication channel, and hormonal molecular communication channel, are introduced. The architecture of is explored to get inspiration for the development of new foundations in molecular communication. Molecular communication inside the human body nanonetworks inside the human body Healthcare applications of Molecular Communication Applications of MC related to health care have can be classified into two groups namely Diagnosis and Treatment. is a therapeutic diagnosis mechanism that has been analytically modeled using molecular communication. ADD (Antibody Mediated Drug Delivery Systems) This will be a cost-effective and reliable drug discovery and optimization model. The accuracy of this model has been confirmed by finite element simulations. In the diagnostics area, nano-sensors, nanophotonics and nano cameras are playing a vital role in the early detection of biological and environmental contaminants in the body. Nanomedicine is the biomedical application that is trending in that top five research areas of nanotechnology. Other biomedical applications of MC include immune system support, medical implants, drug delivery systems, health monitoring, and correcting genetic disorders to name a few. More applications of nanonetworks can be found . here Principles of Molecular Communication Molecular communication protocol consists of information molecules that contain information to be transmitted, sender bio-nano machines that send information molecules, and receiver bio-nano machines that receive information molecules. Other types of molecules might be included in the system such as transport molecules which move information molecules, guide molecules which guide the movement of transport molecules, and interface molecules for selective transport of information molecules. Image Source: Science Direct Different phases of molecular communication are described below: In this phase, sender nanomachines encodes the information into the information molecules in various forms. Encoding: : In this phase sender bio nanomachine releases information molecules in the environment. Sending : It is the phase in which molecules travel from sender nanomachine towards receiver nanomachine. This transport can be either passive or active. Passive transport is the through the diffusion of molecules in the environment without chemical energy, whereas in active transport information molecules bind to molecular motors. Propagation : Transmitted molecules are received from the aqueous medium in this phase usually with the help of chemical receptors. Receiving : In this phase, the captured molecules are decoded by receiver nano-machines into the form of chemical energy. Decoding Molecular Communication Interface A vesicle-based communication interface provides a mechanism to encapsulate messenger molecules in an aqueous medium, which aims to transport different types of information molecules in diverse propagation environments. A liposome is an artificially created spherical vesicle composed of a phospholipid bilayer membrane surrounding a discrete aqueous compartment (an inner aqueous phase); its diameter can be controlled from tens of nanometers to tens of micrometer. The liposomal structure compartmentalizes information molecules from the propagation environment and provides a generic architecture to transport diverse types of information molecules, independent of their biochemical and physical properties. The liposomal structure also protects information molecules from deformation. Molecular Propagation System In Molecular Communication, there exist three types of propagation mechanisms. Walkway, Flow-based and Diffusion-based mechanisms. 1.  In walkway-based propagation, molecules connect the transmitter to the receiver by traveling on predefined pathways using carrier substances such as molecular motors. 2. In a flow-based mechanism, molecules flow towards the receiver in guided and predicted manner. In a flow-based mechanism, carrier entities can also be used for molecular transmission instead of showing random entities. An example of a flow-based mechanism is the bloodstream in the human body (hormones). 3. In the diffusion mechanism, molecules flow in the fluidic medium and can be affected by non-predictable turbulence present in the fluidic medium. Diffusion-based molecular communication is considered to be the most practical means of wireless communication between nanomachines. In diffusion-based communication, the molecules flow from a high concentration region to low concentration regions in fluidic medium. Key components of diffusion-based transmission are transmitter, propagation system and a receiver. Each transmitter has a limited store of messenger molecules. Whenever there is a message to be transmitted, a transmitter emits a specific amount of molecules into the environment. Transmitter: Molecules are released or absorbed by means of molecular flux, which is able to modulate the molecular concentration rate at the transmitter location as a function of time. The amplitude of the transmitted signal defined by the number of released messenger molecules. Each nano-robot has a 0.1 µm3 tankage and has an initial source of 108 molecules of which Q=103-104 molecules are emitted at a time. The prorogation is accomplished by a thermally activated diffusion mechanism, in which flux is achieved from regions of high to low concentrations via random collision with the underlying medium. Messenger molecules propagate in two ways; normal diffusion where interactions and collision among emitted particles is neglected and anomalous propagation where movements of emitted particles can be affected by the collision among interaction among other particles. Propagation system: The particle reception process is accomplished by sensing the messenger molecules in the receiver sensing area. The receiver sensing area is a small surface around the receiver in which the messenger molecules bind to the receiver chemical receptors with high probability. The received nano-machine biochemically reacts with the received information molecules and an electrical inner cell signal is produced as the output. Receiver: Lessons to be learned There is a plethora of applications of Molecular Communication (MC) that need researchers' attention. Research in MC is still in its infancy and there a lot of open research challenges that need to be addressed. MC displays potential
 for enabling complex applications of nanotechnology that
 require the collaboration of very small entities (nanomachines). 
MC in nano-scale communication has three advantages. MC has a number of advantages over traditional electromagnetic communication. It requires very low energy, MC is able to decrease the size of the antenna, and MC has biocompatible characteristics for in vivo systems, as long as harmless molecules are
 used. (Featured image source : Juraj Lenhard from Pixabay )

An Intro to Nanotechnology: What is Molecular Communication? 

Image Source: H2020 FET projects in nanotechnologies Nano network is a novel information and communication technology, which is based on the communication of nano-scale devices. does not merely refer to the reduction in transmitter and receiver size, but it’s the communication protocols between these nanodevices that are novel. Nano network Traditional wired and wireless networking paradigms use electromagnetic signals as a transmission medium. Whereas, nanonetworks utilize to send and receive messages between nanomachines. message encoded molecules The Nanosize Conundrum Computer science has always been used to solve real-world problems. The progress in computing machines can be marked with an increase in computational power and a reduction in the size of the machines. Nanotechnology has made the manufacturing of computing machines possible that are even smaller than human hair and can only be seen under a microscope. These devices are called nanodevices or nanomachines. are capable of solving real-world problems more efficiently as compared to their macro size counterparts. Nanomachines When the computing devices are downscaled to nano size, they grow in number and can work more efficiently together as compared to a single big machine. Image Source: Research gate The network of nanodevices is analogous to the swarm of bees. One bee is weak and only perform simple chores. But when these bees encounter an attack, they quickly mobilize to form a swarm and attack the predator jointly. Similarly,  nanomachines are resource-constrained devices in terms of computation and power due to their minute size. The full potential of these devices can be explored by interconnecting them into the form of a network. A nano network will aid in overcoming the constraints of a single nanodevice by allowing it to coordinate, share, and fuse information with other devices in the network. There are several promising applications of nanonetworks that can provide solutions in the biomedical, industrial, environmental, and military fields. Nano network applications Nanonetworks applications are unlimited and are used extensively in almost every field. These are classified into the following broad groups. Biomedical applications of nanonetworks Image Source: Medium The size of nanodevices makes them feasible for several biomedical and health monitoring applications including diagnostics, treatment, and prevention of diseases. Another advancement in the field of healthcare is the nanomachine being deployed inside the human body which can remotely be controlled from the nanoscale and over the internet by an external user (healthcare provider). In the diagnostics area nano-sensors, nanophotonics and nano cameras are playing a vital role in the early detection of biological and environmental contaminants in the body. Connecting the human body with macro-scale devices and networks for data collection is not feasible as 1) the high-frequency electromagnetic waves can interfere with the normal functioning of the body and 2) large devices connected to the body will hinder the mobility of the patient. Thanks to nanotechnology the size of machines is so small that these can be injected into the bloodstream or even swallowed. Don't worry! these machines are non-invasive and made from biocompatible and biodegradable materials.  Following are some of the potential biomedical applications: Immune system support Medical implants Drug delivery systems Continuous Health monitoring Correcting genetic disorders Nanomedicine Industrial applications of nanonetworks Nanodevices are showing potential in a number of industrial and consumer good applications.  Interconnected nanomachines are used by the video gaming industry for increased thrill and realistic gaming experience. Micro-electromechanical System (MEMS) aims at developing small scale systems e-g Lab-on-a-chip (LOC). A LOC is typically a few micrometers to millimeters in size. It provides the functionality of transporting molecules from one location to another, mixing different types of molecules, and separating specific kinds of molecules from a mixture. Some other industrial and consumer goods applications are listed below Food and water quality control Functionalized materials and fabrics Sports and video gaming Cosmetics Military Applications of nanonetworks Nanotechnology also has several applications in the military field. Dense deployment of nanonetworks in large areas helps in monitoring and actuation of the battlefield while soldier performance can be monitored by deploying nano-networks in the human body. Nanodevices such as imperceptible nano cameras, ultrasonic nano phones, and biological nano-sensors are devices that show potential in battlefield monitoring and actuation. Environmental Applications of Nanodevices Image Source: Agriculture News The bio-inspired nature of nanotechnology makes it feasible to detect and sense contaminated materials found in nature. The problem of handling and dumping garbage is increasing around the world; this problem can be dealt with by a biodegradation process that uses nano-networks. Animals and biodiversity control process can be realized with nanonetworks using pheromones. This results in interaction with certain animals and also to control their presence in particular areas. Nanonetworks can also be used to monitor air, thus controlling air pollution and nano filters can be developed to improve air quality and remove harmful materials from the air. ARCHITECTURE OF NANONETWORKS Nano network is comprised of several nano-scale devices such as nano transmitter, nano receiver, nano router, and other specialized nanodevices to perform exclusive tasks like sensing, actuation, monitoring, and control. The composition of nanonetworks depends upon the prospective application requirements. For example, a biomedical application like nano surgeries might need specialized nanodevices like nanorobots along with essential components of the nano network. Nano transmitters, nano receivers, and nano routers are the essential components of nanonetwork, however, a nanonetwork might not necessarily contain all of the below-mentioned nanodevices. Following is a detailed classification of nanomachines that can be part of nanonetworks according to application requirements. Nano Transmitters Nano transmitters are specialized nanomachines that are capable of sending encoded message molecules into the nano network environment. Information molecules can be sent into the environment by two types of nano transmitters, pre-encoded nano transmitters, and generic nano transmitters. Pre-encoded nano transmitters have encoded molecules stored in their compartment at the time of fabrication. Nanosystems that can be used as pre-encoded nano transmitters are artificially synthesized cells, genetically engineered biological cells, or artificial cells. For example, lipid-based liposomes (i.e., molecule carrying enclosure) and polymer-based nanospheres and nanocapsules can be used as nano transmitters. Generic nano transmitters mimic natural cells such as bacteriophage, viruses, etc. Unlike pre-encoded nano transmitters, the message molecule is synthesized inside the nano transmitter according to the trigger signal. has been done by University of Cambridge researchers. A detailed review of physical design options for nano transmitters Nano Receiver A nano receiver detects the transmitted signal from the environment, further processes it, and produces an output signal in response. Same as nano transmitters, nano receivers take up the molecule from the environment, process it via a predefined biochemical algorithm that decodes the signal. The decoded signal is then used to initiate an appropriate response in the environment e-g, stopping, initiating drug delivery sessions, synthesize or release some molecules, internal reconfiguration, or other biochemical reactions. Nano Sensor A nanosensor is a simple nanodevice that senses the environment to detect the presence of biological components or changes in environmental conditions like light, pH level, temperature. Nanosensors can coordinate the changes in the environment with other nanodevices, which may work as a trigger signal to initiate an appropriate response. Nano Robot Nanorobots are nanodevices that have actuation capabilities in addition to the sensing ability. Nanorobot consists of sensors, actuators, information processing unit, memory unit, and power unit that make nanorobots a powerful device. Nanorobots can manipulate the surrounding environment by performing actuation functionalities like precise delivery of therapeutic molecules to diseased cells. Nano controller Nano controllers are specialized nanodevices that are used to monitor and control other devices in the nano network. Their tasks may include monitoring the drug release process to make sure that a controlled amount of drug is released or to initiate/stop a process in the nano network. Nano Router Nano routers are more sophisticated nanodevices in terms of power, storage, and computation capacity. They are in charge of aggregating and processing information coming from various nanodevices in the network. Nanotechnology will let us build computers that are incredibly powerful. We'll have more power in the volume of a sugar cube than exists in the entire world today - Ralph Merkle

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