Abstract Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts to automate neural network design through learning network architecture and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using techniques from NAS, which enables search space design and architecture search. We use this framework to justify the importance of circuit architecture in quantum machine learning by generating a family of Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset, GTZAN. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. Finally, we implement the framework as an open-source Python package to enable dynamic circuit creation and facilitate circuit search space design for NAS.
Abstract Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts to automate neural network design through learning network architecture and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using techniques from NAS, which enables search space design and architecture search. We use this framework to justify the importance of circuit architecture in quantum machine learning by generating a family of Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset, GTZAN. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. Finally, we implement the framework as an open-source Python package to enable dynamic circuit creation and facilitate circuit search space design for NAS.
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.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multilayer 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 highdimensional 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.
Quantum Machine Learning (QML) utilizes the principles of quantum computations to improve the performance of learning by the use of parameterized quantum circuits and variational algorithms. But despite the qubit number, circuit depth, and patterns of entanglement, creating the optimal quantum circuit architectures for particular machine learning applications remains an important problem. Fixed circuits, or classical Neural Architecture Search (NAS), are not enough to transfer knowledge to other tasks and/or to search the quantum search space efficiently. In this context, this paper presents an Adaptive Quantum Circuit Architecture Search (AQ CAS) framework that dynamically generates and optimizes quantum circuit architectures using a hybrid search algorithm that integrates reinforcement learning, evolutionary operators, and evaluation without gradient. AQ CAS involves a task-specific feedback mechanism to eliminate those candidates that perform poorly and concentrate the search on those candidates with good performance. The framework is evaluated on benchmark data sets such as Iris, synthetic classical ML tasks, and hybrid quantum-classical tasks, and compared to fixed circuits, existing quantum NAS methods, and classical machine learning models. Experimental results indicate that AQ CAS can achieve higher accuracy for the task, using fewer qubits, fewer layers, and less training time, which further shows the need for adaptivity and task specialization. These results inform us about efficient hybrid classical-quantum pipelines, and give a glimpse of the directions that adaptive architecture search might take in enabling scalable and practical QML deployment.
Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints. Existing approaches rely on manually tuned architectures that fail to account for the collaboration between classical preprocessing, phase encoding, and photonic circuit structure, limiting both accuracy and hardware compatibility. In this paper, we propose a neural architecture search framework for hybrid photonic quantum-classical models that combines genetic algorithm-based search with learnable quantum phase encoding to systematically explore the joint design space of classical and quantum components. Our framework encodes 19 hyperparameters across six gene groups and evolves a population of hybrid architectures using group-based crossover, per-gene mutation, and elitism, evaluating each candidate on a short training budget before full retraining of the best found design. We evaluate our framework on two image classification benchmarks, Digits and MNIST, achieving final validation accuracies of 99.44% and 98.78%, respectively, with first-principles execution time estimates on the Quandela Ascella photonic QPU projecting single-image inference at ~67 ms (Digits) and ~149 ms (MNIST). Our quantum contribution analysis further shows that the photonic layer extracts non-redundant features orthogonal to the classical pathway, providing a measurable accuracy advantage over classical-only baselines. Our results demonstrate that automated architecture search is both practical and impactful for hybrid photonic systems, opening the way for systematic design space exploration of quantum AI on photonic devices.
Quantum neural architecture search aims to automate the design of parameterized quantum circuits (PQCs) within quantum neural networks (QNNs), where learning performance is highly sensitive to the placement and type of quantum gates. However, applying traditional NAS techniques to quantum circuits presents significant challenges, including barren plateaus, noise accumulation, device-specific constraints, and the high computational cost of independently evaluating all candidate architectures. To address these limitations, this paper proposes Q-RLONAS, a two-stage quantum NAS framework that integrates deep reinforcement learning (DRL) with one-shot NAS to efficiently explore and optimize PQC structures. In the Stage #1, DRL determines the optimal gate placement by leveraging a reward function that considers performance, noise, barren plateaus, and hardware constraints. In the Stage #2, a one-shot NAS method is employed to select effective quantum gate types using weight sharing within a supernet, significantly reducing training overhead. Experimental results on the mini-MNIST dataset demonstrate that Q-RLONAS achieves high accuracy while satisfying gate placement constraints, outperforming existing approaches in terms of efficiency for quantum circuit design.
Neural Architecture Search (NAS) has revolutionized deep learning by automating the design of optimal neural network architectures. However, classical NAS methods suffer from high computational costs and inefficient search processes. In this research, we introduce Quantum-Inspired Neural Architecture Search (Q-NAS), leveraging quantum computing paradigms such as quantum annealing and variational quantum circuits (VQC) to optimize NAS for edge-device deployment. We explore hybrid quantum-classical frameworks that improve search efficiency and reduce energy consumption. Our work provides comparative analyses, proposes novel quantum search strategies, and benchmarks Q-NAS against traditional NAS methods. Experimental results demonstrate significant reductions in search time and computational costs while maintaining or improving model accuracy.