Post-variational quantum neural networks (PV-QNNs) shift learning from parameterized quantum circuits to a classical head, but their performance hinges on selecting effective fixed ansätze. Manual templates or random circuit picks are brittle and scale poorly. We propose an automated, genetic-algorithm (GA) framework for PV-QNN ansatz ensemble selection. The GA searches a discrete circuit grammar (single-/two-qubit gates under topology and depth constraints) to assemble small ensembles whose quantum features are extracted via classical shadows and consumed by a shallow classical classifier. On Fashion-MNIST and MNIST (2 vs 6), GA-selected ensembles consistently outperform random and heuristic baselines while using about 40% fewer circuits; our efficiency metric, accuracy per circuit, improves by 74%. We also observe stable cross-dataset transfer, indicating that the discovered circuits capture task-relevant structure beyond a single training distribution. These results demonstrate that evolutionary search is a practical route to high-utility, low-footprint PV-QNNs, improving accuracy and sample efficiency without any quantum parameter training, and strengthening PV-QNN viability on near-term hardware.
Post-variational quantum neural networks (PV-QNNs) shift learning from parameterized quantum circuits to a classical head, but their performance hinges on selecting effective fixed ansätze. Manual templates or random circuit picks are brittle and scale poorly. We propose an automated, genetic-algorithm (GA) framework for PV-QNN ansatz ensemble selection. The GA searches a discrete circuit grammar (single-/two-qubit gates under topology and depth constraints) to assemble small ensembles whose quantum features are extracted via classical shadows and consumed by a shallow classical classifier. On Fashion-MNIST and MNIST (2 vs 6), GA-selected ensembles consistently outperform random and heuristic baselines while using about 40% fewer circuits; our efficiency metric, accuracy per circuit, improves by 74%. We also observe stable cross-dataset transfer, indicating that the discovered circuits capture task-relevant structure beyond a single training distribution. These results demonstrate that evolutionary search is a practical route to high-utility, low-footprint PV-QNNs, improving accuracy and sample efficiency without any quantum parameter training, and strengthening PV-QNN viability on near-term hardware.
Post-Variational Quantum Neural Networks (PVQNNs) eliminate the need for quantum parameter optimization by employing fixed, non-parametric circuits and shifting all learning to the classical domain, thereby avoiding barren plateaus entirely. However, the performance of PV-QNNs critically depends on the choice of ansatz architecture. In this work, we propose an automatic ansatz optimization framework based on Genetic Algorithms (GAs) that searches for optimal circuit ensembles under discrete constraints of topology, depth, and gate types. Quantum features are extracted efficiently via classical shadows and processed by a shallow classical classifier, preserving the post-variational property. Experimental results on the Fashion-MNIST (0 vs 4) classification task demonstrate that GA-selected ensembles outperform all baselines, achieving 89.0 % accuracy with 8 qubits, exceeding random and manually designed templates by3−5%. Moreover, GA ensembles achieve up to1.9×higher accuracy per circuit and2×higher accuracy per twoqubit gate, while maintaining shallower circuits (6.0 depth vs. 8.6). These results indicate that evolutionary search effectively identifies compact, information-rich quantum circuits, enabling scalable and resource-efficient post-variational learning.
Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limitations in scalability, flexibility, and adaptability, often producing circuits that are poorly matched to the specific problem or quantum hardware. In this work, we propose the Evolutionary eXploration of Augmenting Quantum Circuits (EXAQC), an evolutionary approach to the automated design and training of parameterized quantum circuits (PQCs) which leverages and extends on strategies from neuroevolution and genetic programming. The proposed method jointly searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints. The method supports both Qiskit and Pennylane libraries, allowing the user to configure every aspect. This work highlights evolutionary search as a critical tool for advancing quantum machine learning and variational quantum algorithms, providing a principled pathway toward scalable, problem-aware, and hardware-efficient quantum circuit design. Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy on most of the benchmark datasets with a limited computational budget, and are able to emulate target circuit quantum states with high fidelity scores.
Hybrid Quantum Neural Networks (HQNNs) combine classical learning with parameterized quantum circuits, but their practical performance is often limited by (i) the noise of Noisy Intermediate-Scale Quantum (NISQ) devices and (ii) the large, discrete design space of quantum circuit architectures. Moreover, HQNNs are commonly trained using a fixed circuit and a single backend, even though deployment frequently targets heterogeneous backends where compilation and execution characteristics may differ. To address these challenges, we propose GAT-QNN, a genetic algorithm (GA)-based framework that trains a macroCircuit (search space) by iteratively sampling microCircuits (subcircuits), training them, and reintegrating their learned parameters into the macroCircuit. After training, we run an independent GA-driven inference stage that evaluates candidate microCircuits using the trained macroCircuit weights and selects top-performing architectures for deployment. This two-stage approach enables backend-aware microCircuit selection without retraining each candidate architecture and can also reduce computational resources (gate count) by deploying smaller microCircuits derived from the macroCircuit. We validate the approach on MNIST classification (four classes) and report consistent 22-23% test accuracy gains for GA-driven inference across multiple backends.
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum circuit architecture, a choice that remains largely manual. Most existing approaches rely on hand-designed or fixed circuit ansätze, requiring circuit structure, gate composition, and qubit connectivity to be specified in advance with no guarantee that they suit the task. This limitation is especially acute in image classification, where quantum circuits must transform features extracted by classical networks while remaining compact enough for practical training, requirements that generic, task-agnostic ansätze are unlikely to satisfy simultaneously. We extend EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification. EXAQC evolves PQCs as intermediate processing modules while retaining classical feature-extraction and prediction layers. On MNIST, Fashion-MNIST, and CIFAR-10, EXAQC achieves 98.42%, 90.62%, and 85.47% accuracy, respectively, while using comparable gate counts to other quantum architecture-search methods. Against classical networks, evolved hybrid models maintain comparable accuracy with substantially fewer trainable parameters, reaching 85.68% on CIFAR-10 with over 25× fewer parameters than a 10-layer CNN. Encoding choice also matters: rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10. These results demonstrate that automated circuit discovery yields compact quantum modules that can replace larger classical components in vision architectures while retaining competitive accuracy.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters.
This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and resource use, while limited qubits often necessitate circuit cutting. Existing quantum architecture search methods primarily optimize accuracy while only heuristically controlling quantum and mostly ignore the exponential overhead of circuit cutting. We introduce QNAS, a neural architecture search framework that unifies hardware aware evaluation, multi objective optimization, and cutting overhead awareness for hybrid quantum classical neural networks (HQNNs). QNAS trains a shared parameter SuperCircuit and uses NSGA-II to optimize three objectives jointly: (i) validation error, (ii) a runtime cost proxy measuring wall clock evaluation time, and (iii) the estimated number of subcircuits under a target qubit budget. QNAS evaluates candidate HQNNs under a few epochs of training and discovers clear Pareto fronts that reveal tradeoffs between accuracy, efficiency, and cutting overhead. Across MNIST, Fashion-MNIST, and Iris benchmarks, we observe that embedding type and CNOT mode selection significantly impact both accuracy and efficiency, with angle-y embedding and sparse entangling patterns outperforming other configurations on image datasets, and amplitude embedding excelling on tabular data (Iris). On MNIST, the best architecture achieves 97.16% test accuracy with a compact 8 qubit, 2 layer circuit; on the more challenging Fashion-MNIST, 87.38% with a 5 qubit, 2 layer circuit; and on Iris, 100% validation accuracy with a 4 qubit, 2 layer circuit. QNAS surfaces these design insights automatically during search, guiding practitioners toward architectures that balance accuracy, resource efficiency, and practical deployability on current hardware.