Classical computers use bits, where they can be either 0 or 1. Quantum computers use qubits, which are the quantum version of bits. A qubit is a two-level system that can be in a blend of 0 and 1 at the same time, and this is called superposition. Two or more qubits can show entanglement, meaning they are statistically linked and have the same outputs regardless of how far they are separated. A quantum circuit is a sequence of operations (gates) applied to qubits, followed by a measurement that returns outputs as classical bits. Current quantum devices are small and noisy, often called NISQ (Noisy Intermediate-Scale Quantum). qubits superposition entanglement quantum circuit gates NISQ Quantum as of today There are three main clusters where quantum studies are focused: •	Chemistry/materials: estimate ground-state energies (input to drug and battery design). Chemistry/materials: •	Optimization: approximate solutions to routing, scheduling, or allocation. Optimization: •	ML/NLP: small- to medium-size demos using hybrid quantum–classical workflows. ML/NLP: We can mention two common patterns for quantum machine learning (QML). Quantum enables us to represent patterns that are not easy (or even impossible) to recognise by classical ML models: Quantum kernels map data into a quantum state. So, it is more like a “fancy feature mapper”, providing a kernel value (similarity score). Later, a standard SVM (Support Vector Machine) or kernel regressor can use the output. Variational circuits have trainable angles that act like a little neural layer. The output is fed into a classical optimizer, such as gradient-based methods, Adam, SPSA, etc. These models are sometimes called VQC (variational quantum classifier) or QNN (quantum neural network). Quantum kernels map data into a quantum state. So, it is more like a “fancy feature mapper”, providing a kernel value (similarity score). Later, a standard SVM (Support Vector Machine) or kernel regressor can use the output. Quantum kernels map data into a quantum state. Variational circuits have trainable angles that act like a little neural layer. The output is fed into a classical optimizer, such as gradient-based methods, Adam, SPSA, etc. These models are sometimes called VQC (variational quantum classifier) or QNN (quantum neural network). Variational circuits have trainable angles that act like a little neural layer. Quantum NLP (QNLP) QNLP is different than NLP as its circuit follows the grammar rules directly rather than pushing a group of words into a black-box and hoping it to get understood. Therefore, grammar rules and structures become explicit in the model. Here are the steps to follow for QNLP: •  A parser splits a sentence into parts such as subject, verb, object etc. •  QNLP builds a circuit matching structure. •  Each word is a small quantum state or block structured based on the grammar •  At the end, we can measure a label, which is sentiment, entailment etc. Tooling exists to parse text and compile circuits in quantum NLP. Lowlight is that the current tasks are small (short sentences, limited vocabulary), but the pipeline is clear and reproducible on the highlights. AI Explainability (XAI) i) Quantum kernel models reuse classical XAI: With a quantum kernel + SVM (support vector machine), it is possible to achieve standard interpretability. Therefore, it is possible to get a clear story while still exploring quantum feature maps. i) Quantum kernel models reuse classical XAI: Support vectors identify training points that determine the boundary. Margins and counterfactuals are used to measure distance to the boundary, which is the minimal input change to flip a class. Feature attributions on inputs are gradients or perturbations computed on raw features, while the kernel remains a black-box similarity produced by the circuit. Support vectors Margins and counterfactuals Feature attributions ii) Grammar-based transparency in QNLP: We can trace how phrase compositions affect the label as parse structure becomes circuit wiring. That provides a more natural explanation since it is similar to the way we talk about language. For example, using QNLP, we can: ii) Grammar-based transparency in QNLP: •	Report which adjective–noun or subject–verb tilted the sentiment or triggered the contradiction. •	Run counterfactuals by swapping a word, recompiling the circuit, and observing label change (or not), •	Log per-composition scores, showing human-readable explanations aligned with syntax in the grammars. Benefits and Limits for ML and NLP Benefits Expressive feature maps (kernels): Quantum circuits are capable of generating similarity functions that classical mapping cannot achieve. Therefore, using quantum low-data or highly structured data regimes can work effectively. Expressive feature maps (kernels): Hybrid modelling is a working tool with quantum and classical methods: Using quantum, variational blocks can encode domain structures such as symmetry and known interactions underneath, and later can be inserted next to classical layers for the task completion. Hybrid modelling is a working tool with quantum and classical methods: Structured language modeling: QNLP’s grammar-driven wiring enables systematic generalisation on small tasks and gives traceability. This way, it is possible to point out which phrase interacted with which and why. Structured language modeling: Limits Quantum hardware size and noise challenges: This is mainly due to the number of qubits available. These small systems are not super stable either. When scaled up, the accuracy and training stability start to degrade. Quantum hardware size and noise challenges: Training is not straightforward: Variational models can get stall (barren plateaus). Mitigations might be required as such shallow circuits, local cost functions, and careful init. Training is not straightforward: Kernel estimation cost: Building a full kernel matrix requires many circuit evaluations, of which the results can be sensitive to feature-map design. Kernel estimation cost: Scale gap in NLP: QNLP is more about structure and explainability. In terms of scale, it has not matched large transformer models on broad benchmarks. Scale gap in NLP: Summary Quantum is still in the early stage, but has already crossed the chasm for productization. Its methods add new feature maps and structure circuits that are useful in small data sets, complex patterns, and well-scoped ML and NLP settings. The clearest near-term value is explainability in the QML and QNLP domains, enabling grammar-based transparency. It is not hard to guess the future once the hardware limitations and accuracy challenges are addressed. Now it is time to focus on understanding quantum circuits, careful baselines, and reproducible traces.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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What Quantum Machine Learning Means for the Future of AI

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