Share Knowledge, Grow Together The future of work is remote, collaborative, and social. With BHyve, employee wisdom is diffused within the workforce to increase productivity, decrease time spent looking for information, and enhance employee learning and engagement. BHyve is the go-to platform for enhanced Remote Workforce Productivity.

BHyve

bhyve

When we first came up with our “big idea” back in 2017, my partner Mike Wagner and I didn’t know how much impact it would have. The idea was simple enough: We wanted to develop a virtual appliance that could discover, provision, and maintain servers from any manufacturer. By 2020, that vision became a reality with the launch of our flagship platform, which we built entirely on open-source technologies. That strategy was working well from the start. Well enough, I should say. We knew we could improve our metal-as-a-service software appliance if we could just find a way to boost performance, ensure higher system uptime, and streamline the architecture. The only way to do that, we realized, was to replace the Linux operating system and KVM virtual machine solution with something that would give us those benefits. What we did know was that we wanted to continue with our strategy of utilizing open source—and ideally tied to a very permissive license and open governance model. That would give us more flexibility as our roadmap unfolded and enable extensive use and modification without stringent (or murky) conditions. Taking the plunge After our due diligence, we transitioned to the Unix-like open source FreeBSD operating system, along with the BSD hypervisor Bhyve. We liked that the FreeBSD project had off-the-shelf capabilities within a single distribution, and had plenty of mission-critical enterprise use cases (Netflix and Sony being two of the most well-known). Together with existing technologies like the Redfish standard, the new and improved platform we’d built (called Mojo) was ready to really take off. With FreeBSD and bhyve fully in place, we were now able to add edge data center provisioning to the platform, which helps discover, configure, and provision bare metal servers and storage within a data center and at the edge. It works really well with another new technology (PhotoISP, which I’ll discuss in a moment) to manage geographically distributed bare metal assets. These open-source changes to our infrastructure proved to be the silver bullet we were looking for. More organizations began to contact us, and we started landing some really interesting use cases, including Major League Baseball. Before the 2021 season, MLB was looking for a way to move from VMware to a containerized environment across 30 data centers. In a nutshell, they needed a way to quickly process 15 different input sources to track all the player and team statistics generated during a game. By more quickly provisioning servers, that data could be quickly gathered at the edge and uploaded into Google Cloud. The Mojo platform, backed by an evolved open-source strategy, helped streamline MLB’s deployment pipelines (cutting deployment time in half). It also simplified the provisioning and management of MLB’s bare metal edge sites and made it really easy to provision Anthos, BIOS, firmware, OS patches, and upgrades. They also saw big performance gains for their GPU-enabled apps and more optimized CI/CD pipelines. Expanding a vision backed by open source Albert Einstein once said that necessity is the mother of all invention. My family lives in the Blue Ridge Mountains, and when COVID hit, word spread quickly that several teachers and students were unable to stream their classes because they had insufficient rural broadband access. Mike and I brainstormed about how we could help, and we immediately recognized how Mojo, paired with a reliable and secure CPE, could solve the problem. That led development of the Photon ISP platform, which delivers routing, firewall, and QoS through the open-source FreeBSD operating system. It also monitors IDS/IPS for wireless and fixed fiber networks and ensures uninterrupted 4G/5G and Starlink failover as a hedge to wireless ISP services. Today, the Photon ISP platform has 50 rural customers, and that number continues to grow. And its uptime is unparalleled; we have FreeBSD CPEs that have been running for more than a year without issue. Speeds have been very consistent, streaming content has been reliable, and there has been almost zero latency. We’re still pushing the limits of what we can do with our optimized open-source infrastructure. Next, we plan to finish moving the remainder of the stack to FreeBSD once a viable OCI-compliant stack becomes available. In the meantime, we still use Linux within Bhyve virtual machines for a few of our services. Simply put, the strategic shift to open-source FreeBSD and Bhyve for our platforms has catalyzed innovation and customer growth. The success with MLB—cutting deployment times and improving performance—is a great example of how the right open-source pieces can have an outsized impact while being far more flexible (not to mention cost-efficient!) than proprietary alternatives. When we first came up with our “big idea” back in 2017, my partner Mike Wagner and I didn’t know how much impact it would have. The idea was simple enough: We wanted to develop a virtual appliance that could discover, provision, and maintain servers from any manufacturer. By 2020, that vision became a reality with the launch of our flagship platform, which we built entirely on open-source technologies. That strategy was working well from the start. Well enough , I should say. enough We knew we could improve our metal-as-a-service software appliance if we could just find a way to boost performance, ensure higher system uptime, and streamline the architecture. The only way to do that, we realized, was to replace the Linux operating system and KVM virtual machine solution with something that would give us those benefits. What we did know was that we wanted to continue with our strategy of utilizing open source—and ideally tied to a very permissive license and open governance model. That would give us more flexibility as our roadmap unfolded and enable extensive use and modification without stringent (or murky) conditions. Taking the plunge After our due diligence, we transitioned to the Unix-like open source FreeBSD operating system, along with the BSD hypervisor Bhyve . We liked that the FreeBSD project had off-the-shelf capabilities within a single distribution, and had plenty of mission-critical enterprise use cases (Netflix and Sony being two of the most well-known). Together with existing technologies like the Redfish standard, the new and improved platform we’d built (called Mojo) was ready to really take off. open source FreeBSD Bhyve Redfish With FreeBSD and bhyve fully in place, we were now able to add edge data center provisioning to the platform, which helps discover, configure, and provision bare metal servers and storage within a data center and at the edge. It works really well with another new technology (PhotoISP, which I’ll discuss in a moment) to manage geographically distributed bare metal assets. These open-source changes to our infrastructure proved to be the silver bullet we were looking for. More organizations began to contact us, and we started landing some really interesting use cases, including Major League Baseball. Before the 2021 season, MLB was looking for a way to move from VMware to a containerized environment across 30 data centers. In a nutshell, they needed a way to quickly process 15 different input sources to track all the player and team statistics generated during a game. By more quickly provisioning servers, that data could be quickly gathered at the edge and uploaded into Google Cloud. The Mojo platform, backed by an evolved open-source strategy, helped streamline MLB’s deployment pipelines (cutting deployment time in half). It also simplified the provisioning and management of MLB’s bare metal edge sites and made it really easy to provision Anthos, BIOS, firmware, OS patches, and upgrades. They also saw big performance gains for their GPU-enabled apps and more optimized CI/CD pipelines. Expanding a vision backed by open source Albert Einstein once said that necessity is the mother of all invention. My family lives in the Blue Ridge Mountains, and when COVID hit, word spread quickly that several teachers and students were unable to stream their classes because they had insufficient rural broadband access. Mike and I brainstormed about how we could help, and we immediately recognized how Mojo, paired with a reliable and secure CPE, could solve the problem. That led development of the Photon ISP platform, which delivers routing, firewall, and QoS through the open-source FreeBSD operating system. It also monitors IDS/IPS for wireless and fixed fiber networks and ensures uninterrupted 4G/5G and Starlink failover as a hedge to wireless ISP services. Today, the Photon ISP platform has 50 rural customers, and that number continues to grow. And its uptime is unparalleled; we have FreeBSD CPEs that have been running for more than a year without issue. Speeds have been very consistent, streaming content has been reliable, and there has been almost zero latency. We’re still pushing the limits of what we can do with our optimized open-source infrastructure. Next, we plan to finish moving the remainder of the stack to FreeBSD once a viable OCI-compliant stack becomes available. In the meantime, we still use Linux within Bhyve virtual machines for a few of our services. Simply put, the strategic shift to open-source FreeBSD and Bhyve for our platforms has catalyzed innovation and customer growth. The success with MLB—cutting deployment times and improving performance—is a great example of how the right open-source pieces can have an outsized impact while being far more flexible (not to mention cost-efficient!) than proprietary alternatives.

How Our Open Source OS Pivot Accelerated Product Innovation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix F: Optimization of optical extraction efficiency An optimal optical external coupling can be chosen to maximize the total conversion efficiency. Equation E9 can be rewritten in terms of quality factors by using the relation κ = ω/Q, which yields For the optics, we have Qo = Qint + Qext, where we separate the intrinsic quality factor Qint, oftentimes fabricationlimited and hence not easily adjustable, from the external coupling quality factor Qex that can be readily engineered through coupler design. The optical extraction efficiency can thus be written in the form where R = Qint/Qex and the total optical quality factor The total efficiency in the low-cooperativity regime is then given by where For a given F, the optimal efficiency is achieved at the critical coupling condition Qex = Qint, or R = 1. [1] H. Haus and W. Huang, Proceedings of the IEEE 79, 1505 (1991). [2] T. Blésin, H. Tian, S. A. Bhave, and T. J. Kippenberg, Phys. Rev. A 104, 052601 (2021). [3] A. Rueda, W. Hease, S. Barzanjeh, and J. M. Fink, npj Quantum Inf. 5, 1 (2019). [4] C. Zhong, Z. Wang, C. Zou, M. Zhang, X. Han, W. Fu, M. Xu, S. Shankar, M. H. Devoret, H. X. Tang, and L. Jiang, Phys. Rev. Lett. 124, 010511 (2020). [5] S. Krastanov, H. Raniwala, J. Holzgrafe, K. Jacobs, M. Lončar, M. J. Reagor, and D. R. Englund, Phys. Rev. Lett. 127, 040503 (2021). [6] L.-M. Duan, M. D. Lukin, J. I. Cirac, and P. Zoller, Nature 414, 413 (2001). [7] M. H. P. Pfeiffer, J. Liu, A. S. Raja, T. Morais, B. Ghadiani, and T. J. Kippenberg, Optica 5, 884 (2018). [8] J. Liu, G. Huang, R. N. Wang, J. He, A. S. Raja, T. Liu, N. J. Engelsen, and T. J. Kippenberg, Nat. Commun. 12, 2236 (2021). [9] H. Tian, J. Liu, B. Dong, J. C. Skehan, M. Zervas, T. J. Kippenberg, and S. A. Bhave, Nat. Commun. 11, 3073 (2020). [10] A. Siddharth, A. Anastasio, G. Lihachev, J. Zhang, Z. Qiu, S. Kenning, R. N. Wang, S. A. Bhave, J. Riemensberger, and T. J. Kippenberg, Hertz-linewidth and frequency-agile photonic integrated extended-DBR lasers (2023), arxiv:2306.03184 [physics]. [11] H. Tian, J. Liu, A. Siddharth, R. N. Wang, T. Blésin, J. He, T. J. Kippenberg, and S. A. Bhave, Nat. Photon. 15, 828 (2021). [12] M. Gao, Q.-F. Yang, Q.-X. Ji, H. Wang, L. Wu, B. Shen, J. Liu, G. Huang, L. Chang, W. Xie, S.-P. Yu, S. B. Papp, J. E. Bowers, T. J. Kippenberg, and K. J. Vahala, Nat. Commun. 13, 3323 (2022). [13] V. B. Braginsky, M. L. Gorodetsky, and V. S. Ilchenko, in Laser Applications, Vol. 2097 (SPIE, 1994) pp. 283–288. [14] A. Schliesser, P. Del’Haye, N. Nooshi, K. J. Vahala, and T. J. Kippenberg, Phys. Rev. Lett. 97, 243905 (2006). [15] K. Leong and J. Mazierska, IEEE Trans. Microw. Theory Tech. 50, 2115 (2002).

Bidirectional Microwave-optical Transduction Based on Integration: Appendix F: Optimization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix C: Optical characterization 1. Resonance linewidths Fitting the hybridized transmission spectrum in Fig. 2c to the coupled mode theory presented in Appendix A 1 and in Ref. [2] yields κr/(2π) = 154 MHz and κl/(2π) = 190 MHz, with κex,l/(2π) = κex,r/(2π) = 60 MHz. 2. Hybridization of optical resonances via integrated thermo-optic heaters For the purpose of rapid characterization of the photonic molecules at room temperature, the bottom electrode of the piezoelectric actuator also serves as an integrated heater to control the relative detuning between the microrings by thermo-optic effect. As seen in Fig. 2a, we pattern Mo to make three electrical connections to the bottom electrode. The top and bottom connections are connected to ground, whereas the central connection is biased to a constant voltage. The finite resistance of Mo at room temperature leads to Joule heating as current flows through the bottom electrode, modifying the refractive index of the Si3N4 waveguide buried directly underneath. 3. Thermal response characterization through cavity-enhanced photothermal spectroscopy. present transducer due to optical pump heating. Specifically, thermally induced refractive index change causes a shift in resonance frequency of the Si3N4 micro-ring, which is detected using cavity-enhanced pump-probe spectroscopy [12]. The intensity-modulated pump addresses a TE resonance, while the probe measures the resulting side-of-fringe modulation of another TE mode. To ascertain the physical mechanism of each cross phase modulation (XPM) process, we sweep the pump modulation frequency to leverage the separation of time scales. Seen in Supplementary Fig. 2, the response exhibits three plateaus. We associate the process at modulation frequencies ≳ 1 MHz with Kerr-induced XPM. The two slower processes at ∼ 100 kHz and ∼ 1 kHz are characteristic of photothermal XPM in quasi-freestanding microresonators such as spheres [13] and toroids [14]. Here, the former “local” time scale can be attributed to thermalization of the mode volume with the acoustic resonator, while the thermalization of the suspended structure with the rest of the chip constitutes the latter “global” time scale. Therefore, it is expected that for future cryogenic operations, employing a pulse-on time (τon) shorter than the local time scale to gate the optical pump will significantly reduce thermal occupancy in the cladding acoustic mode. The pulse repetition rate (frep) and hence pulse-off time can then be chosen based on the available cooling power.

Bidirectional Microwave-optical Transduction Based on Integration: Appendix C: Optical

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix E: Characterization of bidirectional transduction 1. Estimation of cooperavity and single-photon coupling rate Given the measured off-chip efficiency, the single-photon coupling rate can be estimated. The off-chip efficiency is given by where ηprobes and ηfiber-chip denote the microwave and optical insertion losses, respectively. The on-chip efficiency can be written as The internal efficiency in the low-cooperativity regime, where the plus and minus signs correspond to the anti-Stokes and Stokes configurations, respectively. The optical and microwave extraction efficiencies The single-photon cooperativity C0 is enhanced by the intracavity photons where the pump detuning is assumed to be small. The input power in the bus waveguide Pwg is related to the power in the input fiber Pin by the fiber-chip coupling efficiency The off-chip efficiency with respect to the microwave probe and optical fiber is thus 2. Estimation of on-chip and internal transduction efficiencies We have achieved η tot = −48 dB at an input pump power of 21 dBm. With the losses quoted in Appendix E 1, the on-chip and internal efficiencies can be estimated. For conversion in either directions, the signal goes through the respective input and output ports exactly once, acquiring an attenuation of η probesη fiber−chip = −7 dB. We therefore have η oc = 7.9×10−5 . Knowing the extraction efficiencies ηo = 35% and ηm = 11%, we further obtain η int = 2×10−3 .

Bidirectional Microwave-optical Transduction Based on Integration: Appendix E: Characterization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix D: Characterization of acoustic resonances The microwave reflection measurements (also known as S11) shown here were measured at room temperature using custom probes (GGB industries nickel-alloy 40A-GS-135-PC-N Picoprobe) and a vector network analyzer (Rhode&Schwarz ZNB-20). The spectrum shown in Fig. 2d is corrected using a calibration substrate (GGB industries CS-8) to remove the phase delay and attenuation introduced by coaxial cables and probes. However, this calibration procedure was only used to get the S-parameter, and was not applied during the total conversion efficiency measurements shown in Fig. 3 and Fig. 4. In a previous version of the transducers and in Ref. [11] where the Band-Aid process (Appendix B) is not employed, the piezoelectric actuator extends beyond the suspended cladding and covers the electrical feedline, causing two detrimental effects for transduction. First, the resulting stray capacitance leads to excess microwave insertion loss. Given the small size of the suspended area (∼ 1000 µm2 ) compared to that of the feedline (≳ 10 000 µm2 ), this effect is readily seen in Supplementary Fig. 3, where the spectrum without the Band-Aid process exhibits reduced background reflection. Second, the part of the actuator on top of the unreleased cladding leads to resonant excitation of HBAR modes extending over the entire Si substrate. These modes have a smaller free spectral range (FSR) of around 19 MHz and a lower microwave extraction efficiency. The theory presented in Appendix A does not correspond to the case of such a multimode system as it assumes being in the sideband-resolved regime, implying the high density of substrate modes is inappropriate for low-noise frequency conversion. The feedline Band-Aid, discussed in Appendix B and shown in Supplementary Fig. 3, enables the removal of the piezoelectric layer above the substrate, leaving only HBAR modes well confined in the oxide cladding. We fit the measured complex reflection to obtain the mechanical quality factor Qm = 284 and microwave extraction efficiency κex,m/κm = 0.11 for the transduction mode [15]. In addition, we simulate microwave reflection of the designed stack geometry using finite-element method (FEM). Fitting the simulated spectrum leads to Qm = 205 and κex,m/κm = 0.61. The discrepancy is attributed to difference in clamping losses and piezoelectric coefficients between the fabricated and simulated devices. We extract the effective mass of the transduction HBAR mode from FEM simulations as

Bidirectional Microwave-optical Transduction Based on Integration: Appendix D: Characterization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix B: Fabrication process flow The piezoelectric actuators are monolithically integrated on Si3N4 waveguides, fabricated using the photonic Damascene process [7, 8]. The 850-nm-thick Si3N4 film is deposited using low-pressure chemical vapor deposition into the Damascene preform, with 2.8 µm thermal oxide below the waveguides. After annealing to drive hydrogen impurities out of the Si3N4 layer, 2.1-µm-thick TEOS oxide and 1.0-µm-thick low-temperature oxide top claddings are deposited and subsequently annealed. The metallic and piezoelectric films—95 nm of Mo, 1.0 µm of AlN and another 95 nm of Mo—are sputtered through foundry services provided by Plasma-Therm [9]. They are then patterned using deep reactive ion etching (DRIE) to form the actuators, ground planes and integrated heaters. We employ the same BandAid process as in Ref. [10] to localize the electrodes atop the suspended cladding. In particular, when connecting the Al feedline to the top electrode by a lift-off process, the bottom electrode is etched back using XeF2 to avoid shortcircuiting the electrodes. The process to suspend the cladding in the vicinity of the actuator is combined with the chip singulation steps of the Damascene process. First, a hole is opened in the middle of the donut-shaped actuator. The chip facets are simultaneously defined where trenches in the SiO2 are etched by C4F8 DRIE between neighboring chips. A second photolithography is performed to protect the facets while leaving the etched holes exposed for further processing, resulting in narrower trenches between chips. The Si substrate is then isotropically etched with SF6 until the SiO2 cladding below the actuator is suspended [11]. Following cladding suspension, the Si substrate is etched anisotropically until the desired chip thickness of 250 µm is reached. A final isotropic Si etch removes the parts of the substrate protruding from the SiO2 facet, providing proper access to the bus waveguide nanotapers with lensed fibers, or even using the butt-coupling scheme. To facilitate this last step, we employ ion-beam etching at a 20◦ tilt angle to remove the passivation layer formed on the hole sidewalls during the Si DRIE. The chips are ultimately released by grinding the back side of the wafer. This convoluted process ensures that all the actuators on the wafer are fully suspended, while the chip facets do not become exceedingly fragile from the undercut.

Bidirectional Microwave-optical Transduction Based on Integration: Appendix B: Fabrication

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix A: Theory 1. Coupled mode theory for optical modes hybridization Using a pair of optical resonances increases the intracavity photon number for a given input optical power, resulting in an enhanced optomechanical interaction. Such an optical doublet is implemented here by evanescently coupling two micro-rings, forming a photonic molecule. Consider two bare optical modes with resonance frequencies ωl and ωr that correspond to the left and right micro-rings, respectively. The two modes couple with a rate J, which yields the Hamiltonian [1] and the equations of motion With Laplace transform, they can be rewritten in frequency domain as 2. Transducer Hamiltonian and input-output relations In terms of bare micro-ring modes, the transducer Hamiltonian is given by Note that the piezo-coupled acoustic mode interacts with only one of the micro-rings. To compute quantities directly related to the experiments, we rewrite the Hamiltonian in terms of the supermodes (Appendix A 1) as Finally, by defining effective optomechanical coupling rates and going into a rotating frame defined by the operator we have in the interaction picture. The time evolution of the bosonic modes can be computed using the Heisenberg-Langevin equations given by We can move to the frequency domain by using the Fourier transform along with the properties We then have 3. Conversion efficiency and added noise For the following analysis, we consider the triply resonant configuration where ω+ −ω− = ωm and the optical pump is on resonance with one of the optical supermodes. First, we consider the anti-Stokes configuration where ∆− = 0 and ∆+ = −ωm. From Eq. A24, we have the input-output relation The equations of motion is given by 4. Estimation of correlated microwave-optical pair generation rates Equations A40 and A43 suggest that entangled pairs of microwave and optical photons can be generated in the Stokes configuration since it gives rise to an effective two-mode squeezing Hamiltonian [3–5]. In order to verify that the HBAR transducer can generate entangled pairs of microwave and optical photons, we compute the on-chip pair generation rate, expected to be equal to the output photon flux from the lower frequency optical mode as in standard spontaneous parametric down-conversion (SPDC) scheme. Integrating over the transduction bandwidth, we have In practice, the pump laser should be filtered for the optical output to be dominated by heralding photons, and other losses from the measurement setup can further reduce the effective rate. For η oc = 7.9×10−5 , κ− = 2π×300 MHz and κm = 2π × 10 MHz, this would corresponds to an on-chip pair generation rate of 190 Hz. Losses in the measurement chain will have to be accounted for to estimate the count rate at the detector. In order to measure entanglement between the microwave and optical fields, the on-chip rate must be higher than the thermal decoherence rate of the acoustic mode κmnth,m(ωm), where the thermal occupancy nth,m(ωm) is given by the Bose-Einstein distribution. For ωm = 2π×3.5 GHz and κm = 2π×10 MHz, the decoherence rate at 800 mK is 43 MHz and 0.5 Hz at 10 mK. Therefore, the transducer needs to be operated in the mixing chamber of the dilution refrigerator, with a pulsed pump to reduce heat load. The total rate of optical heralding is hence gated by the pump duty cycle.

Bidirectional Microwave-optical Transduction Based on Integration: Appendix A: Theory

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency III. DISCUSSION Non-classically correlated microwave-optical photon pairs can be generated through spontaneous parametric down-conversion in our transducer. These correlated photon pairs constitute a key ingredient toward entangling distant quantum processors via the Duan-LukinCirac-Zoller protocol [4, 45]. With the current device parameters, the pair generation rate on chip amounts to 190 Hz, which is well above the thermal decoherence rate for 3.5 GHz at 10 mK as explained in Appendix A 4. However, losses in the measurement setup specific to such an experiment, as well as the gating of the optical pump required to alleviate the effective heat load, are expected to limit further the final heralding rate. Quasi-free-standing structures, such as 1D OMCs, do not readily thermalize and are thus more susceptible to heating effects. Even though our transducer also utilizes a suspended acoustic resonator, it may be feasible to operate at a higher duty cycle than what is presented in Fig. 4. It has been shown that a buffer gas environment can facilitate thermalization of a 1D OMC, at the cost of increased damping of the mechanical breathing mode [46]. On the contrary, the HBAR mode of interest here should be relatively insensitive to such viscous damping, as the acoustic wave propagates predominantly along the longitudinal direction inside the cladding. Therefore, a sample cell affixed to the mixing chamber flange and filled with buffer superfluid helium—an inviscid fluid that is both an excellent thermal conductor and electrical insulator—could potentially improve the heralding rate without hampering the conversion efficiency [47]. The present design is also suited for tasks beyond quantum state transfer. The transducer can be a key component of photonic interconnects for superconducting qubits [7, 8], where laser lights routed through optical fibers are used to encode microwave signals directly inside the dilution refrigerator during qubit readout. It may be challenging for traveling-wave EOMs to attain the requisite half-wave voltage to be competitive in noise performance against conventional all-electrical readout scheme utilizing high-electron-mobility transistor amplifiers [8]. Our triply resonant approach affords a path towards a scalable optically controlled cryogenic waveform generator for both qubit readout and control. The multimode nature of the transducer is particularly suited for frequency-multiplexed dispersive readout. Transduction leveraging multiple HBAR modes is already demonstrated in Fig. 3e. One can additionally utilize line-type instead of point-type dimer coupling for the photonic molecule, which gives rise to a dispersion in the supermode splitting [48]. In combination with the multitude of acoustic overtones, a single transducer can therefore support several spectrally distinguishable triply resonant systems that serve as qubit multiplex channels. Finally, since the transducer requires no superconducting element to function, it may be pertinent for applications in classical microwave photonics. Optimization of design and fabrication should engender further improvement on device performance. The conversion efficiency, proportional to microwave extraction efficiency (Eq. 2), can be improved by replacing AlN with scandium-doped AlN [49], which features a piezoelectric coefficient d33 about five times larger than AlN [50, 51] while preserving CMOS compatibility. There is likewise additional upside on the photonics (Appendix C 1). Bending radiation loss can be significantly reduced by increasing the micro-ring radius r. In particular, intrinsic quality factor exceeding 107 has been demonstrated with an r approximately ten times larger using the same fabrication process [52]. It may be anticipated that the efficiency gain here may be chiefly compensated by the reduction in optomechanical coupling rate g0 ∼ 1/ √ r. Nevertheless, by thinning the waveguide to 200 nm that approaches half of the acoustic wavelength, g0 is expected to remain unchanged. As in the thick-core waveguide fabricated using the photonic Damascene process, such a thin-core waveguide on a subtractive manufacturing platform is expected to exhibit negligible bending loss [53]. Assuming critical coupling conditions (Appendix F), we therefore expect another 1.5- time gain in η tot. Finally, optical insertion loss can be nearly eliminated by employing a spot-size converter to facilitate mode-matching with lensed fiber [54–56]. Combining these improvements, our transducer could achieve a total efficiency η tot = 6 × 10−3 . In conclusion, we have designed, fabricated, and characterized a compact piezo-optomechanical microwaveoptical transducer that integrates wafer-scale, CMOScompatible HBAR and Si3N4 photonic technologies. Free of any superconducting components, this triply resonant transducer attains a bidirectional off-chip photon number conversion efficiency of 1.6 × 10−5 and a bandwidth of 25 MHz, with an input pump power of 21 dBm at room temperature. Multimode transduction leveraging distinct HBAR modes have been demonstrated, suggesting prospects for designing frequency-multiplexed photonic interconnects for superconducting qubits. Realistic design and fabrication improvements may even allow access to experiments in the quantum regime.

Bidirectional Microwave-optical Transduction Based on Integration: Discussion

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency ACKNOWLEDGMENTS The authors would like to thank Amirali Arabmoheghi, Nils Johan Engelsen and Junyin Zhang for experimental assistance and fruitful discussions. This work was supported by the Air Force Office of Scientific Research under award no. FA8655-20-1-7009, Swiss National Science Foundation under grant agreement no. 204927, and European Research Council (ERC) under the EU H2020 research and innovation programme, grant agreement no. 835329 (ExCOM-cCEO). This work was further supported by United States Air Force Research Laboratory Award no. FA8750-21-2-0500, as well as United States National Science Foundation’s International Collaboration Supplements in Quantum Information Science and Engineering Research for RAISE-TAQS Award no. 18-39164. A.S. acknowledges support from the European Space Technology Centre with ESA Contract No. 4000135357/21/NL/GLC/my. Samples were fabricated in the Center of MicroNanoTechnology (CMi) at EPFL and Birck Nanotechnology Center at Purdue University. AUTHOR CONTRIBUTIONS T.B. designed the device. R.N.W, A.A. and H.T. fabricated the device. T.B., A.S. and W.K. conducted the experiments. T.B. and W.K. wrote the paper with input from all authors. T.J.K. and S.A.B. supervised the project. [1] K.-L. Lee, B. Sedighi, R. S. Tucker, H. Chow, and P. Vetter, J. Opt. Commun. Netw. 4, A59 (2012). [2] Q. Cheng, M. Bahadori, M. Glick, S. Rumley, and K. Bergman, Optica 5, 1354 (2018). [3] J. I. Cirac, P. Zoller, H. J. Kimble, and H. Mabuchi, Phys. Rev. Lett. 78, 3221 (1997). [4] L.-M. Duan, M. D. Lukin, J. I. Cirac, and P. Zoller, Nature 414, 413 (2001). [5] D. Awschalom, K. K. Berggren, H. Bernien, S. Bhave, L. D. Carr, P. Davids, S. E. Economou, D. Englund, A. Faraon, M. Fejer, S. Guha, M. V. Gustafsson, E. Hu, L. Jiang, J. Kim, B. Korzh, P. Kumar, P. G. Kwiat, M. Lončar, M. D. Lukin, D. A. Miller, C. Monroe, S. W. Nam, P. Narang, J. S. Orcutt, M. G. Raymer, A. H. Safavi-Naeini, M. Spiropulu, K. Srinivasan, S. Sun, J. Vučković, E. Waks, R. Walsworth, A. M. Weiner, and Z. Zhang, PRX Quantum 2, 017002 (2021). [6] J. M. Chow, J. M. Gambetta, A. D. Córcoles, S. T. Merkel, J. A. Smolin, C. Rigetti, S. Poletto, G. A. Keefe, M. B. Rothwell, J. R. Rozen, M. B. Ketchen, and M. Steffen, Phys. Rev. Lett. 109, 060501 (2012). [7] F. Lecocq, F. Quinlan, K. Cicak, J. Aumentado, S. A. Diddams, and J. D. Teufel, Nature 591, 575 (2021). [8] A. Youssefi, I. Shomroni, Y. J. Joshi, N. R. Bernier, A. Lukashchuk, P. Uhrich, L. Qiu, and T. J. Kippenberg, Nat. Electron. 4, 326 (2021). [9] R. W. Andrews, R. W. Peterson, T. P. Purdy, K. Cicak, R. W. Simmonds, C. A. Regal, and K. W. Lehnert, Nat. Phys. 10, 321 (2014). [10] A. P. Higginbotham, P. S. Burns, M. D. Urmey, R. W. Peterson, N. S. Kampel, B. M. Brubaker, G. Smith, K. W. Lehnert, and C. A. Regal, Nat. Phys. 14, 1038 (2018). [11] M. M. Wolf, D. Pérez-García, and G. Giedke, Phys. Rev. Lett. 98, 130501 (2007). [12] R. D. Delaney, M. D. Urmey, S. Mittal, B. M. Brubaker, J. M. Kindem, P. S. Burns, C. A. Regal, and K. W. Lehnert, Nature 606, 489 (2022). [13] J. Bochmann, A. Vainsencher, D. D. Awschalom, and A. N. Cleland, Nat. Phys. 9, 712 (2013). [14] A. Vainsencher, K. J. Satzinger, G. A. Peairs, and A. N. Cleland, Appl. Phys. Lett. 109, 033107 (2016). [15] W. Jiang, R. N. Patel, F. M. Mayor, T. P. McKenna, P. Arrangoiz-Arriola, C. J. Sarabalis, J. D. Witmer, R. V. Laer, and A. H. Safavi-Naeini, Optica 6, 845 (2019). [16] M. Forsch, R. Stockill, A. Wallucks, I. Marinković, C. Gärtner, R. A. Norte, F. van Otten, A. Fiore, K. Srinivasan, and S. Gröblacher, Nat. Phys. 16, 69 (2020). [17] W. Jiang, C. J. Sarabalis, Y. D. Dahmani, R. N. Patel, F. M. Mayor, T. P. McKenna, R. Van Laer, and A. H. Safavi-Naeini, Nat. Commun. 11, 1166 (2020). [18] M. Mirhosseini, A. Sipahigil, M. Kalaee, and O. Painter, Nature 588, 599 (2020). [19] S. Hönl, Y. Popoff, D. Caimi, A. Beccari, T. J. Kippenberg, and P. Seidler, Nat. Commun. 13, 2065 (2022). [20] W. Jiang, F. M. Mayor, S. Malik, R. Van Laer, T. P. McKenna, R. N. Patel, J. D. Witmer, and A. H. SafaviNaeini, Nat. Phys. , 1 (2023). [21] M. J. Weaver, P. Duivestein, A. C. Bernasconi, S. Scharmer, M. Lemang, T. C. van Thiel, F. Hijazi, B. Hensen, S. Gröblacher, and R. Stockill, An integrated microwave-to-optics interface for scalable quantum computing (2022), arxiv:2210.15702 [physics, physics:quantph]. [22] S. Meesala, S. Wood, D. Lake, P. Chiappina, C. Zhong, A. D. Beyer, M. D. Shaw, L. Jiang, and O. Painter, Nonclassical microwave-optical photon pair generation with a chip-scale transducer (2023), arxiv:2303.17684 [quantph]. [23] M. Tsang, Phys. Rev. A 81, 063837 (2010). [24] M. Tsang, Phys. Rev. A 84, 043845 (2011). [25] C. Javerzac-Galy, K. Plekhanov, N. R. Bernier, L. D. Toth, A. K. Feofanov, and T. J. Kippenberg, Phys. Rev. A 94, 053815 (2016). [26] L. Fan, C.-L. Zou, R. Cheng, X. Guo, X. Han, Z. Gong, S. Wang, and H. X. Tang, Sci. Adv. 4, eaar4994 (2018). [27] J. Holzgrafe, N. Sinclair, D. Zhu, A. Shams-Ansari, M. Colangelo, Y. Hu, M. Zhang, K. K. Berggren, and M. Lončar, Optica 7, 1714 (2020). [28] T. P. McKenna, J. D. Witmer, R. N. Patel, W. Jiang, R. V. Laer, P. Arrangoiz-Arriola, E. A. Wollack, J. F. Herrmann, and A. H. Safavi-Naeini, Optica 7, 1737 (2020). [29] Y. Xu, A. A. Sayem, L. Fan, C.-L. Zou, S. Wang, R. Cheng, W. Fu, L. Yang, M. Xu, and H. X. Tang, Nat. Commun. 12, 4453 (2021). [30] M. Li, H. Liang, R. Luo, Y. He, J. Ling, and Q. Lin, Optica 6, 860 (2019). [31] I. Diniz and R. de Sousa, Phys. Rev. Lett. 125, 147702 (2020). [32] Y. Xu, W. Fu, Y. Zhou, M. Xu, M. Shen, A. A. Sayem, and H. X. Tang, Phys. Rev. Appl. 18, 064045 (2022). [33] D. Zhu, L. Shao, M. Yu, R. Cheng, B. Desiatov, C. J. Xin, Y. Hu, J. Holzgrafe, S. Ghosh, A. Shams-Ansari, E. Puma, N. Sinclair, N. Sinclair, C. Reimer, M. Zhang, and M. Lončar, Adv. Opt. Photonics 13, 242 (2021). [34] A. Rueda, F. Sedlmeir, M. C. Collodo, U. Vogl, B. Stiller, G. Schunk, D. V. Strekalov, C. Marquardt, J. M. Fink, O. Painter, G. Leuchs, and H. G. L. Schwefel, Optica 3, 597 (2016). [35] W. Hease, A. Rueda, R. Sahu, M. Wulf, G. Arnold, H. G. Schwefel, and J. M. Fink, PRX Quantum 1, 020315 (2020). [36] R. Sahu, W. Hease, A. Rueda, G. Arnold, L. Qiu, and J. M. Fink, Nat. Commun. 13, 1276 (2022) [37] L. Qiu, R. Sahu, W. Hease, G. Arnold, and J. M. Fink, Nat. Commun. 14, 3784 (2023) . [38] R. Sahu, L. Qiu, W. Hease, G. Arnold, Y. Minoguchi, P. Rabl, and J. M. Fink, Science 380, 718 (2023) . [39] T. Blésin, H. Tian, S. A. Bhave, and T. J. Kippenberg, Phys. Rev. A 104, 052601 (2021) . [40] H. Tian, J. Liu, B. Dong, J. C. Skehan, M. Zervas, T. J. Kippenberg, and S. A. Bhave, Nat. Commun. 11, 3073 (2020) . [41] H. Tian, J. Liu, A. Siddharth, R. N. Wang, T. Blésin, J. He, T. J. Kippenberg, and S. A. Bhave, Nat. Photon. 15, 828 (2021) . [42] M. H. P. Pfeiffer, J. Liu, A. S. Raja, T. Morais, B. Ghadiani, and T. J. Kippenberg, Optica 5, 884 (2018). [43] J. Liu, G. Huang, R. N. Wang, J. He, A. S. Raja, T. Liu, N. J. Engelsen, and T. J. Kippenberg, Nat. Commun. 12 , 2236 (2021) . [44] J. Liu, H. Tian, E. Lucas, A. S. Raja, G. Lihachev, R. N. Wang, J. He, T. Liu, M. H. Anderson, W. Weng, S. A. Bhave, and T. J. Kippenberg, Nature 583, 385 (2020) . [45] S. Krastanov, H. Raniwala, J. Holzgrafe, K. Jacobs, M. Lončar, M. J. Reagor, and D. R. Englund, Phys. Rev. Lett. 127, 040503 (2021) . [46] I. Shomroni, L. Qiu, D. Malz, A. Nunnenkamp, and T. J. Kippenberg, Nat. Commun. 10, 1 (2019). [47] A. D. Kashkanova, A. B. Shkarin, C. D. Brown, N. E. Flowers-Jacobs, L. Childress, S. W. Hoch, L. Hohmann, K. Ott, J. Reichel, and J. G. E. Harris, J. Opt. 19, 034001 (2017) . [48] S. Kim, K. Han, C. Wang, J. A. Jaramillo-Villegas, X. Xue, C. Bao, Y. Xuan, D. E. Leaird, A. M. Weiner, and M. Qi, Nat. Commun. 8, 372 (2017) . [49] M. Akiyama, T. Kamohara, K. Kano, A. Teshigahara, Y. Takeuchi, and N. Kawahara, Adv. Mater. 21, 593 (2009) . [50] M. Akiyama, K. Kano, and A. Teshigahara, Appl. Phys. Lett. 95, 162107 (2009) . [51] T. Yanagitani and M. Suzuki, Appl. Phys. Lett. 105 , 122907 (2014) . [52] J. Liu, A. S. Raja, M. Karpov, B. Ghadiani, M. H. P. Pfeiffer, B. Du, N. J. Engelsen, H. Guo, M. Zervas, and T. J. Kippenberg, Optica 5, 1347 (2018) . [53] A. Siddharth, A. Anastasio, G. Lihachev, J. Zhang, Z. Qiu, S. Kenning, R. N. Wang, S. A. Bhave, J. Riemensberger, and T. J. Kippenberg, Hertz-linewidth and frequency-agile photonic integrated extended-DBR lasers (2023), arxiv:2306.03184 [physics] . [54] T. Zhu, Y. Hu, P. Gatkine, S. Veilleux, J. BlandHawthorn, and M. Dagenais, IEEE Photonics J. 8, 1 (2016) . [55] Z. Yao, Y. Wan, Y. Zhang, X. Ma, and Z. Zheng, Opt. Commun. 475, 126301 (2020) . [56] B. Bhandari, C.-S. Im, K.-P. Lee, S.-M. Kim, M.-C. Oh, and S.-S. Lee, IEEE Photonics J. 12, 1 (2020) .

Bidirectional Microwave-optical Transduction Based on Integration: Acknowledgments

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency D. Pulsed transduction Quantum-enabled operation, where the added noise referred to the transducer input is less than one quanta, would require concurrently attaining high conversion efficiency and low output noise. As the three-wave mixing interaction is parametric in nature, the associated cooperativity and hence efficiency can in principle be enhanced by boosting the optical pump power. A potential trade-off is nevertheless the subsequently increased thermal noise. While the high resonance frequency of the transduction HBAR already helps suppressing this noise to some extent, additionally employing a pulsed pump presents a number of utilities. Reducing the integrated optical power serves as an effective measure to mitigate thermal load onto the cryostat as well as pump-induced noise while maintaining high peak power. Quantumenabled operation has in fact been achieved with this strategy by a bulk electro-optic transducer employing Watt-scale optical pumping [36]. Gate-based superconducting quantum computers too function inherently in the pulsed regime. As such, we characterize our transducer in both frequency and time domains with a pulsed optical pump to evaluate its compatibility with these cryogenic microwave circuits as well as its potential for quantum-enabled operation. To study pulsed bidirectional transduction, we choose a pulse-on time τon = 1 µs and a repetition rate frep = 100 kHz. We program the pulse sequence such that the increase in temperature, and hence acoustic mode thermal occupancy due to optical heating, is expected to be inconsequential (Appendix C 3). First, we measure the up-conversion efficiency, summarized in Fig. 4a, using the same heterodyne method as in the CW case (Fig. 3). The pulsed optical pump mediates transduction of a CW microwave input into a pulsed optical output. This optical pulse comprising both the up-converted output and pump is then down-mixed by the LO, resulting in a microwave pulse whose frequency content can be probed via an ESA. The measured efficiency exhibits reasonable agreement with CW data. Additionally, the down-converted microwave pulse envelope in the time domain provides an independent measure of the transducer bandwidth—a key metric for qubit-photonic interconnects [7, 8]. With the pulsed pump set in the twomode squeezing configuration, an optical input pulse is converted on-chip into a microwave pulse with a carrier frequency ωEOM = ωm through difference frequency generation. The time-domain dynamics of the pulse envelope is captured through phase-sensitive demodulation using a digital lock-in amplifier. As shown in Fig. 4b, we observe that the pulse envelope is consistent with the step response of the lock-in integrator, which has a bandwidth of 5 MHz, or an RC time constant τRC = 30 ns. The lock-in bandwidth therefore sets a lower bound for the transducer bandwidth.

Bidirectional Microwave-optical Transduction Based on Integration: D. Pulsed Transduction

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency C. Bidirectional microwave-optical transduction We measure the off-chip efficiency η tot of the microwave-to-optical (up-conversion) and optical-tomicrowave (down-conversion) processes as a function of off-chip CW input optical pump power Pin. Compared to the on-chip efficiency η oc, this efficiency also accounts for the loss channels of the microwave and optical ports. First, we study the transducer in the triply resonant configuration. For up-conversion, the optical sideband generated from the microwave input, detuned by ωin = ωm from the pump frequency, is measured via a self-calibrated heterodyne detection method. The off-chip optical output containing both the pump (ωL) and the sideband (ωs) is combined with a local oscillator (LO; ωLO) and detected. By placing the LO at a frequency ωLO = (ωL + ωs) /2 + δ such that the photodetector response is constant over a frequency span of δ, we determine the sideband power relative to the pump. Picking off a fraction of the optical output then enables the determination of its absolute power and, by extension, that of the sideband with a power meter. The measured optical sideband power is compared to that of the microwave input to yield η tot. In the case of down-conversion, the optical input is generated by modulating the phase of the pump with an electrooptic modulator (EOM) driven by a microwave source of frequency ωEOM = ωm. Only one of the resulting sidebands is admitted into the photonic molecule and transduced, as the other is far off-resonance. The converted microwave power is directly probed with an electrical spectrum analyzer (ESA). Summarized in Figs. 3a and c, bidirectional transduction processes corresponding to both the effective beam-splitter (ωL = ω−; anti-Stokes) and two-mode-squeezing (ωL = ω+; Stokes) interactions are investigated. We reach a maximal η tot of −48 dB at 21 dBm pump for each configuration. Knowing the port losses, we estimate an on-chip efficiency η oc = −41 dB (Appendix E). Accounting for the extraction efficiency η ext, we further obtain an internal conversion efficiency η int = 2 × 10−3 from Eq. 2. Furthermore, we deviate from triple resonance to map out the transduction bandwidth. First, for a pump still resonant with one of the optical supermodes, the input microwave frequency ωin and input optical detuning (controlled by EOM drive frequency ωEOM) are varied for up- and down-conversion, respectively. Shown in Fig. 3b and d, transduction leveraging the main transduction mode exhibits a full width at half maximum (FWHM) of 25 MHz. The multimode nature of the transducer is further manifest in Fig. 3e, where the pump is in the beam-splitter configuration but slightly detuned from aˆ−. An additional transduction peak in η tot with a FWHM of 10 MHz is observed around ωin = 2π × 3.165 GHz, which corresponds to another HBAR one FSR away from the main transduction mode ωm = 2π × 3.480 GHz. The engineering degrees of freedom such as cladding and actuator thickness (HBAR FSR), and optical dispersion (supermode splitting as a function of optical wavelength) in the present system offer possibilities for frequencymultiplexed transduction.

Bidirectional Microwave-optical Transduction Based on Integration: C. Bidirectional

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency B. Device characterization We perform metrological characterization to affirm the integrity of the stack. Shown in Fig. 2a, the region of suspended cladding is first identified through optical micrography; the released substrate below leads to a contrast in the image. To further access the layer structure, we use focused ion beam milling to create an opening on the device surface at the suspension site. This opening provides sufficient clearance to directly image the cladding acoustic resonator, pictured in Fig. 2b, confirming the removal of silicon. We then characterize the transducer as an optoelectronic network with one microwave port, two optical ports, and one auxiliary electrical DC port. Each optical port comprises an edge-coupled lensed fiber and on-chip bus waveguide terminated by 1D nanotapers designed for TE-polarized light, yielding a fiber-to-fiber coupling efficiency of −8 dB. For adjusting the frequency splitting between optical supermodes, a bias voltage is applied to the DC port to drive either the piezoelectric actuator [44] or an integrated thermo-optic heater placed in the vicinity of the micro-rings (Appendix C 2). The former affords no additional heat load and is hence cryogenic-compatible, whereas the latter provides an easy alternative for fast room-temperature characterization. The avoided mode crossing characteristic of a photonic molecule is exemplified in Fig. 2c. We access the HBARs through the microwave port. Calibrated high-frequency probes are used to contact the top and bottom electrode pads of the piezoelectric actuator, effectively linking the acoustic resonator with a transmission line. The reflection spectrum (Fig. 2d) reveals predominantly one single series of HBARs with a free spectral range (FSR) of 320 MHz, which corresponds to the acoustic length of the cladding. The transduction acoustic mode at 3.48 GHz exhibits a typical total linewidth of 13 MHz with a microwave extraction efficiency of κex,m/κm ≈ 11%. The microwave response is distinct from that of an identical stack composition with unreleased substrate. There, a periodic envelope corresponding to the cladding modes that are more strongly coupled to the microwave is superimposed over the full-stack HBAR response (Appendix D and Ref. [40]). The absence of these full-stack modes provide another piece of evidence of successful cladding suspension. Finally, we study the device acousto-optic response in the triply resonant configuration, where the transducer effectively operates as a resonant single-sideband modulator. The optical output containing both the pump at the symmetric-mode frequency and generated sideband at the antisymmetric-mode frequency are mixed by a photodetector. Such a beat-note spectrum shown in Fig. 2e displays response peaks aligned with the HBAR frequencies in Fig. 2d, demonstrating three-wave mixing.

Bidirectional Microwave-optical Transduction Based on Integration: B. Device Characterization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency II. RESULTS A. Physics and design Figure 1 delineates the principle of operation of the present transducer, while the theoretical formalism is detailed in Appendix A. The requisite nonlinear interaction for microwave-optical transduction is a parametric threewave mixing process described by standard optomechanical Hamiltonian Through the bilinear piezoelectric interaction, the acoustic resonance (ˆb) is coupled to the itinerant transmission line mode (cˆin) of the same frequency ωin = ωm ≈ 3.5 GHz. This frequency matches the detuning of the optical pump from the optical resonance (aˆ). Specifically, an optical pump addressing the red (blue) side of the resonance induces an effective beam-splitter (two-modesqueezing) interaction between aˆ and ˆb, as illustrated by the signal flow graph in Fig. 1a (1b). The primary figure of merit for a transducer is its efficiency, defined as the number of output photon number for each input photon. An efficient device necessitates strong nonlinear interaction between the modes of interest, as well as ease of coupling to these modes internal to the device. Our design addresses these two aspects. First, the internal efficiency, depends solely on the three-wave mixing cooperativity C = 4g 2 0n/¯ (κoκm). Here, n¯, κo, and κm denote the photon number in the optical cavity, and the total optical and acoustic linewidths of the transduction modes respectively. The single photon optomechanical coupling rate g0 = − (∂ωa/∂x) xZPF, a product of the optical cavity frequency-pull parameter and zero-point displacement of the acoustic wave. In Eq. 1, the plus and minus signs in the denominator correspond to the scenario where the pump is red- and blue-detuned (“anti-Stokes” and “Stokes” processes), respectively. Using a triply resonant configuration as in Refs. [26–29] enhances the intracavity photon number n¯ for a given off-chip input pump power Pin, or corresponding photon flux n˙ in = Pin/ (ℏωL). The improved photon pumping efficiency can be explicitly seen by considering a “hot” cavity with coupling rate κex,o driven at a detuning ∆ = ±ωm, yielding

Bidirectional Microwave-optical Transduction Based on Integration: A. Physics and Design

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Coherent interconversion between microwave and optical frequencies can serve as both classical and quantum interfaces for computing, communication, and sensing. Here, we present a compact microwave-optical transducer based on monolithic integration of piezoelectric actuators atop silicon nitride photonic circuits. Such an actuator directly couples microwave signals to a high-overtone bulk acoustic resonator defined by the suspended silica cladding of the optical waveguide core, which leads to enhanced electromechanical and optomechanical couplings. At room temperature, this triply resonant piezo-optomechanical transducer achieves an off-chip photon number conversion efficiency of 1.6 × 10−5 over a bandwidth of 25 MHz at an input pump power of 21 dBm. The approach is scalable in manufacturing and, unlike existing electro-optic transducers, does not rely on superconducting resonators. As the transduction process is bidirectional, we further demonstrate synthesis of microwave pulses from a purely optical input. Combined with the capability of leveraging multiple acoustic modes for transduction, the present platform offers prospects for building frequency-multiplexed qubit interconnects and for microwave photonics at large. I. INTRODUCTION Modern data centers have seen rapidly increasing traffic that motivates an overhaul of existing network infrastructures. As optical fibers support nearly lossless transport and high bandwidths, twisted-pair copper deployment has shrunk in favor of optical interconnects. Energy-efficient optical transceivers [1] and optical network architectures [2] are being explored in parallel to accommodate emerging data- and resource-intensive applications. In an analogous fashion, processing quantum information in superconducting circuits and networking via photonic interconnects has been envisioned as an effective strategy to address the scalability challenges in advancing quantum technologies [3–5]. The scheme, featuring the full universal set of microwave quantum gates [6] with vanishing thermal occupancy and loss of the optical channels, calls for the development of microwaveoptical transducers to bridge the two energy scales that differ by more than four orders of magnitude. Aside from facilitating the networking of remote quantum processors, these transducers may enable fully optical control and readout of microwave qubits [7, 8]. The subsequent replacement of coaxial lines bridging room temperature and cryogenic environments by optical fibers is expected to significantly ease the space and heat load constraints in dilution refrigerators, opening up a path toward upscaling processor units housed in a single fridge. Efficient frequency conversion requires a nonlinear interaction stronger than the coupling to loss channels. The highest conversion efficiency to date has been achieved using an electro-optomechanical approach [9]. There, a silicon nitride membrane interacts with an optical mode of a free-space Fabry-Pérot cavity via radiation pressure and simultaneously serves as the top plate of a capacitor, parametrically coupling the mechanics to the microwave resonator. By virtue of the high resonator quality factors and pump power handling capability, 47% of input photons can be interconverted between optical and microwave domains [10], approaching the 50% efficiency required for attaining finite quantum capacity [11]. This highly efficient transducer has enabled optical dispersive qubit readout with negligible excess backaction [12]. The low-frequency (MHz) mechanical intermediary nevertheless limits the transduction bandwidth and leads to appreciable added noise even at dilution refrigerator temperature. To address these drawbacks, piezo-optomechanical transducers based on optomechanical crystals (OMC) have been developed [13–22], where a tightly confined high-frequency (GHz) mechanical mode and a co-localized optical mode can interact at a vacuum optomechanical coupling rate g0 ∼ 2π × 500 kHz. The trade-off lies instead in the sophistication required for microwave-phonon wave matching that significantly increases design complexity, as well as thermo-optic instability that constrains the intra-cavity photon number. An on-chip efficiency of 5% has recently been reported on such a platform [20]. However, low thermal conductance of these suspended quasi-one-dimensional structures hinders correlated microwave-optical photon pair generation at a practical rate. Cavity electro-optic modulators constitute a conceptually simpler approach where Pockels effect directly mediates microwave-optical interaction [23–25]. On-chip realizations employing planar superconducting microwave resonators, benefited from the deep sub-wavelength mode volume of the vacuum electric field, to reach single photon electro-optic coupling rates g0 ∼ 2π × 1 kHz [26– 29]. However, the material science of χ (2) crystals poses additional challenges. Photorefractive effects—observed, for instance, in LiNbO3 thin films [30]—hamper optical power handling, while piezoelectric loss and scattered optical photons degrade the quality factor of superconducting resonators [31, 32]. The difficulty of producing smooth sidewall surfaces in the workhorse Pockels material, LiNbO3 , through dry etching results in propagation losses an order of magnitude above the absorption limit [33]. As a result, the maximum on-chip efficiency achieved with integrated electro-optic transducers exceeds just 2% [26]. In the spirit of Ref. [9], a bulk transducer comprising a mm-size mechanically polished LiNbO3 whispering gallery mode (WGM) resonator coupled to a 3D superconducting microwave cavity has proved competitive, trading g0 for improved power handling and quality factors [34, 35]. Taking one step further with pulsed optical pumping, the system has demonstrated not only a hallmark 14.4% total efficiency but also electro-optic dynamical backaction [36, 37] and microwave-optical quadrature entanglement [38]. With the aforementioned design trade-offs in mind, we present a new piezo-optomechanical transducer based solely on wafer-scale, CMOS-compatible fabrication processes. We utilize a low-loss silicon nitride (Si3N4 ) photonic molecule, as well as multiple GHz high-overtone bulk acoustic resonances (HBAR) parametrically coupled to the optical modes. Endowed with power handling capabilities superior to existing integrated transducers, the device fits compactly within a 100 µm-by-50 µm footprint, in contrast to state-of-the-art bulk designs [10, 36]. In addition, the transduction HBAR modes are more readily coupled to the microwave signal, unlike OMC transducers. We demonstrate bidirectional microwave-optical transduction with a bandwidth of 25 MHz and total efficiency up to 1.6 × 10−5 by pumping with 21 dBm of off-chip optical power in continuous-wave (CW) operation. The device is also characterized with a pulsed optical pump, which constitutes the first step toward optical control and readout of qubits, as well as heralded microwave-optical photon pair generation. The simple design, ease of fabrication, robust operation, and compact form factor anticipate wide applicability in quantum technologies and microwave photonics at large.

Bidirectional Microwave-optical Transduction Based on Integration: Introduction

As an open-source enthusiast, I am always interested in the functioning of the open-source ecosystem and the public distribution of software. I recently attended MumbaiFOSS, a conference organized by FOSS United and FOSSEE to promote Free and Open Source Software in Mumbai. The FOSSMumbai conference happened yesterday on the 11th of March 2023, at IIT Bombay. In this blog, I share my experiences and takeaways from the conference, including informative talks on building reliable APIs, digital personal data protection, public policies, and the importance of open-source beyond just yearly programs and swag. About MumbaiFOSS is a collective effort of FOSS United and FOSSEE to revive the spirit of Free and Open source software in Mumbai. MumbaiFOSS is a non-profit foundation that promotes a ecosystem in India. It offers funding and grants to FOSS projects, organizes events to empower open-source developers, and hosts a not-for-profit learning platform . FOSS United Free and Open Source Software Mon School The , or simply , is a project pioneered in IIT Bombay that promotes using Free and Libre Open Source Software (FLOSS) in academics and research. It conducts a plethora of activities to improve education and research quality. Free/Libre and Open Source Software for Education FOSSEE Foss United partnered with FOSSEE to bring together developers, policymakers, and open-source enthusiasts for a conference. MumbaiFOSS was about FOSS (of course!), its issues and trends, and FOSS policies. This was a complete package of happiness for someone who has just scratched the surface of the open-source monolith. Prologue My two college mates accompanied me, coincidentally having the same name - Pratik. The journey from the main gate to the VMCC auditorium felt like a lifetime, but we arrived on time. We were welcomed with a yummy breakfast until the conference started. I expected at least 100 people to be present, but to my surprise, the numbers were relatively low, though satisfactory. Traveling in Mumbai on early morning weekends has the power to change people's desires. The Conference, in Brief Vishal Arya, the swiss army knife of FOSS United, and Riya Mathew, community manager of FOSS United, made the opening note. They told us the story behind FOSS United, the purpose of MumbaiFOSS, and their journey from the beginning. Before the opening note, Prof. Kannan Moudgalaya of IIT Bombay gave an overview of FOSSEE, Spoken tutorials, and projects FOSSEE promotes. Apart from this, we had nine talks, one-panel discussion, and one open discussion throughout the day. From informative talks on building reliable APIs and digital personal data protection to a fireside chat on tech policy, this conference offered a complete package of happiness for anyone interested in the open-source monolith. Read on to discover more about MumbaiFOSS and the importance of open-source beyond just yearly programs and swag. Building Reliable APIs with Apache APISIX by Navendu Pottekkat by Ravi Dwivedi and Arun Mathai Prav: a community-owned chat app by Sumanto Kar and Nagesh Karmali eSim developed by FOSSEE by Amogh Lele Demystifying WebRTC by Ashish Sahni Data Fabric by Poruri Sai Rahul Let's discuss the Digital Personal Data Protection Bill 2022 by Bhavesh Bhatt Transforming Web Apps with Machine Learning by Venkatesh Hariharan FOSS United's Public Policy Initiatives by Kaustubh Maske Patil Self Hosting 101 - Reasons to self-host and nifty tools - a panel discussion between Vaishnavi, Manish, Saurav, Ashish, Dhiraj, Rohit, and Anas Khan Open Source: Supporting, Contributing, and Evangelising by Kailash Nadh and Venkatesh Hariharan Fireside chat: Why engineers should care about tech policy? Venky's talk on FOSS United's Public Policy Initiatives was my favorite. Hearing him talk about his experience, with interesting stories and analogies, was indeed insightful. Fireside Chat This was the main event of the conference. It was a half an hour discussion between Kailash Nadh, Rishabh Rushabh Mehta, and Venkatesh Hariharan. The discussion was mainly focused on new tech policies and our stand on them. How tech policies are changing, why we should care about it, how politicians and political agendas shape these policies, and how we can take steps towards making an impact were a few things that were discussed. It's important to have more technologists involved in public policies than lawyers. And, I Met Kailash Nadh! Kailash Nadh has been CTO of Zerodha since 2013. I was fascinated when I read that Zerodha's engineering team only comprises 33 people, and nobody has left the team in all these years for better opportunities. Damn, I was excited to meet this man! As everybody started leaving the auditorium after the conference, I saw him talking to Rushabh and a few team members. I went there and asked him for a photo together, and he gently agreed. I couldn't stop but express my admiration towards his work at Zerodha and how much I admire his startup. He thanked me, and I went on cloud nine. Afterthoughts Open source is a lot more than I used to think. It is beyond a few yearly open-source programs, goodies/swags, and adding certificates on LinkedIn. It is beyond contributing to projects backed by large corporations and projects that enhance developer/web experience. It is also about being free from proprietary software, empowering and encouraging the government and societies to use FOSS, reaching more students, educating them, and many more. MumbaiFOSS was the first conference I attended. And it was absolutely fantastic! Everything was planned and managed so well that I couldn't find any issues to rant on. And the food was tasty, too! Kudos to the operations and management team for pulling it off so well. Epilogue I plan to attend more such conferences. I searched for some while going back home, and I might go there if everything aligns well. Of course, I will update you all! I am most active on , where I post such updates. Twitter You can also subscribe to my E-mail newsletter to have my next blog in your inbox! Until then, keep contributing! 🌿 Also published here

MumbaiFOSS: My Experience, Thoughts, and Opinions

Animated Card Create an animated card in HTML & CSS. To change the styling of a card based on the element’s hover state, focus state, or any of its descendants state, use the pseudo-class selectors and . :hover :focus-within Using , hide the extra content of the card and show it on hover. overflow:hidden HTML Code <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Animated Card</title>
    <link rel="stylesheet" href="style.css">
</head>
<body>
    <div class="background-image"></div>
    <div class="card">
        <img src="https://images.wallpaperscraft.com/image/single/honda_s2000_honda_car_322685_1280x720.jpg"/>
        <h3>Hover Me</h3>
        <div class="focus-content">
          <p>Lorem ipsum dolor sit, amet consectetur adipisicing elit. Eveniet quia fugit labore dolores sint reiciendis perspiciatis<br/>
        </div>
    </div>
</body>
</html> CSS Code body{
    display: flex;
    justify-content: center;
    align-items: center;
    height: 100vh;
}
.background-image {
    position: fixed;
    left: 0;
    right: 0;
    display: block;
    background-image: url(https://images.wallpaperscraft.com/image/single/honda_s2000_honda_car_322685_1280x720.jpg);
    -webkit-filter: blur(4px);
    -moz-filter: blur(4px);
    -o-filter: blur(4px);
    -ms-filter: blur(4px);
    filter: blur(4px);
    width: 100%;
    height: 100%;
    background-size: cover;
}
.card {
    width: 300px;
    height: 280px;
    padding: 0;
    box-shadow: 0 2px 4px 0 rgba(0,0,0,0.1);
    border-radius: 8px;
    box-sizing: border-box;
    overflow: hidden;
    background-color: #fff;
    z-index: 1;
  }
  
  .card * {
    transition: 0.3s ease all;
  }
  
  .card img {
    margin: 0;
    width: 300px;
    height: 224px;
    object-fit: cover;
    display: block;
  }
  
  .card h3 {
    margin: 0;
    padding: 12px 12px 48px;
    line-height: 32px;
    font-weight: 500;
    font-size: 20px;
  }
  
  .card .focus-content {
    display: block;
    padding: 8px 12px;
  }
  
  .card p {
    margin: 0;
    line-height: 1.5;
  }
  
  .card:hover img, .card:focus-within img {
    margin-top: -80px;
  }
  
  .card:hover h3, .card:focus-within h3 {
    padding: 8px 12px 0;
  } Output: Preview https://animated-card-bay.vercel.app/ Blog https://beginners-developer.blogspot.com/ Portfolio https://ejaazrkhan.github.io/portfolio/ Also published . here

How to Make an Animated Card Using CSS Animation

Artificial Intelligence is a lot more than a tech buzzword these days. This technology has disrupted almost every industry within a decade. Every company wants to implement this cutting edge technology in its system to cut costs, save time, and make the overall process more efficient with automation. Initially, AI and were as simple as Simple Perceptron models and Single layered neural networks. With the passage of time and focused research, we have complex neural networks with multi-layered architectures. Companies are using algorithms such as LSTMs, GANs, variational autoencoders in their software and services.. deep learning algorithms Given below are the hottest topics in AI and the companies working on them. Get ready to get awestruck after looking at these innovations. However, if you want to explore further, you can explore it here tl motion simulator Natural Language Processing - NLP is one of the hottest topics in AI. NLP deals with language (textual data) and performs tasks such as translation, transliteration, semantic analysis, development of chatbots, text mimicry, text to speech, etc. In the present day scenario, NLP is very crucial for organizations because it has disrupted customer support by automating the process and reducing human intervention. Natural Language Processing In addition to this, NLP techniques are also utilized in developing marketing strategies on the basis of semantic analysis done on text mined from social media platforms. In its crux, most of the NLP algorithms are based on complex deep neural networks such as RNNs, LSTMs, and GRUs. To draw a basic intuition, you can look at them as conventional neural networks that emphasize on previous data inputs (i.e. memory). Moveworks and Dialogflow are one of the leading companies based on NLP. Let us take a brief look at them. Dialogflow Dialogflow is Google’s child company which focuses on latest research on NLP techniques and develops modules, APIs, and platforms related to it. You can integrate Dialogflow’s services to Amazon Alexa, Siri, Cortana, and of course - Google Home. Its services use Long Short Term Memory (LSTM) and Recurrent Neural Networks to execute tasks related to texts and languages. Dialogflow is well known for its chatbot service. In addition to this, companies such as Dominos, Mercedes, Giorgio Armani are taking the help of Dialogflow for embedding chatbots and text to speech services within their system. You can easily read Dialogflow’s documentation and have your own chatbot ready on your website/mobile application in a matter of minutes. Moveworks Moveworks is a $200 Million company based in Mountain View, California. It was founded by Bhavesh Shah, Jiang Chen, Vaibhav Nivargi, and Varun Singh. The foundation of every chatbot and NLP product is laid by RNN or its successors. In its core, Moveworks service is also based on their custom recurrent neural networks and LSTM network trained on relevant data. Moveworks provides customer support automation and NLP solutions for business enterprises. It has served giants such as Nutanix, Autodesk, and Western Digital. If you want to cut down the IT support and automate it to save costs, you must definitely consider Moveworks. Convolutional Neural Networks or CNNs are the most widely used deep learning algorithms. CNN algorithm was a slight modification to conventional neural networks, which further emerged as a revolutionary concept in the field of artificial intelligence. Today, CNNs are used mainly for object detection, face recognition, computer vision, and forecasting techniques. Convolutional Neural Networks Convolutional neural networks involve the stacking of layers in a deep neural network with operations such as padding, convolution, scaling, etc. on the input image. The best part about CNN is that the images are converted into arrays (almost similar to the 1-dimensional matrix) and mathematical operations take place which finally results in the desired output. To implement CNN, you just need a lot of data and a computer to train your AI. Here are a couple of companies that are working on computer vision and CNNs. Matterport Matterport came into limelight when their R&D team published a paper on Mask RCNN. The company is based in Sunnyvale, California is one of the patrons of computer vision products. Moreover, they have developed an efficient computer vision algorithm used for detecting objects (Mask RCNN) which is a successor of Fast RCNN and Faster RCNN. It uses coordinates of bounding boxes and masking techniques to identify objects on which the model is trained. Apart from object detection, matterport’s Mask RCNN is also used in video calling applications to change the background. Currently, they’ve integrated augmented reality with deep learning and have invented one of the finest 3-D cameras of modern times. Matterport manufactures 3-D cameras that can be used for visualizing structures of houses, give virtual tours, and display an engaging floor plan. Neurala Based in Boston, this machine learning and software development company caters to the needs of organizations looking for process automation and computer vision solutions. Neurala’s models are used on more than 50 million devices across the globe. CB Insights has recognized Neurala amongst ‘100 Most Promising AI Companies’. Thanks to its Ph.D. holding founders who made Neurala’s AI software run on light devices including smartphones. They provide services on a wide range of devices including PCs, smartphones, drones, robots, and smart devices. These were some of the most reliable and best companies which might help you in building AI-based systems. Before blindly trusting any company on the internet, you must also take a look at these firms which might help you in cutting costs and provide you a state of the art service. Many AI developers have found that CBD has helped their clarity and consistency when developing algorithms, one of the leaders in this field has actually published an article talking about how CBD can assist with focus while programming. Wtphemp offers that are completely free from THC. Wtphemp CBD products

A Deep Learning Overview: NLP vs CNN

When doing good, brings you good I recently received a package from Amazon. This isn’t totally out of the ordinary. We have a prime subscription and order quite a lot from the online retailer. I didn’t recall ordering anything recently though. I logged into my account and checked, and there wasn’t an open order. I opened the package and saw what was delivered … I definitely would have remembered ordering this … There was no message in the package so I was stumped, where did this awesome drone come from? Later that day I received a slack message from a friend, asking if I’d received a package that day. Off course I put 2 & 2 together and got to pretty quickly to 5. I have yet to meet , something that needs to be fixed, but I’ve known him for a couple of years now and consider him a good friend. Eric Willis He posted to (my first project on ), and that random act has led to some amazing things. Interviewed Product Hunt Product Hunt Shortly after Interviewed was launched, Eric launched a website called which collected blog posts about successful product launches on Product Hunt. Maker Success Unfortunately the website had some performance issues and was having trouble handling the traffic from Product Hunt. Since Eric isn’t a developer, I offered to lend a hand. In true hacker fashion, rather than fixing the issues with the CMS that Eric had selected, I decided instead to just rebuild the website from scratch, and the following day we relaunched . Maker Success (Maker Success) When Eric started his next project: Maker Hunt, a slack community for Product Hunt makers, I was thrilled to be involved. In helping to build the Maker Hunt community with Eric and many other volunteers, I got the opportunity to know people that I would not even have even met let alone worked with. Since I’d had the chance to get to know before we talked about his idea for a website listing marketing resources, I was happy to help him. It didn’t really matter what the idea was to me: he was passionate about it, and I wanted to help. Ben Tossell A few weeks later that led to the launch of . The website was very well received, joining the 1000 vote club on . Product Hunt was so impressed in-fact that Ben now works as a Community Manager there! Marketing Stack Product Hunt By this time I’d become hooked on Product Hunt, and launched a number of other projects there. I’d set myself the target of being the #1 Maker. I even purchased a t-shirt from a project by , but I wouldn’t wear it until I made it to number 1. Maker Tees Daniel Kempe In case you were wondering ... Yes I am wearing my #1 maker tshirt today! // @danielkempe @ProductHunt — @mubashariqbal It didn’t take long for me to get to the top of the , and I’m still there! maker leaderboard Getting to know Daniel thru his projects and the Maker Hunt community, led me to be involved with . Daniel had a great idea for helping people keep their social media timelines full of awesome, relevant content. Unfortunately he didn’t know anyone who could help him turn his idea into a functioning project, so of course I volunteered to help. Quuu After a couple of weeks of hacking we had the MVP for Quuu ready to go, and go it did. Quuu has become a very popular social media marketing tool, and a solid business. This was just the start of a journey that I could write an entire book about. I’ve done more projects with Ben and Daniel, not to mention: , , , and many others. Jonny Nastor Dylan Feltus Bhavesh Patel Seth Louey In doing all these projects I’ve never expected anything in return, I just enjoy working with great people and turning their ideas into reality. There might not be a direct return for taking part in these projects, but the karma roundabout spins quickly. Being selected as is an intangible, invaluable reward, even if it did come with a . Once the weather improves, I’m going to enjoy flying my drone and find a real roundabout I take a spin on. 2016 Product Hunt maker of the year Golden Kitty Thanks again Eric for the gift, and I look forward to doing more projects with friends old and new.

Stories, News & Mentions about BHYVE

Bhyve’s Stories on HackerNoon