Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Neural Networks

EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification

Yangyang Li, Ruijiao Liu, Xiaobin Hao, Ronghua Shang, Peixiang Zhao, Licheng Jiao

Published Sep 28, 2023• 45 citations
Publisher
DOI
10.1016/j.neunet.2023.09.040

Related papers

CQNAS: Convolutional quantum hybrid neural network architecture search for image classification

Desheng Kong, Jiaying Jin, Xiaohai Cui +5 more

Neurocomputing•Mar 6, 2026•2 citations

Quantum-enhanced deep neural network architecture for image scene classification

Avinash Chalumuri, Raghavendra Kune, Suresh Kannan +1 more

Quantum Information Processing•Nov 1, 2021•34 citations

SPA-QNAS: Improving Search Efficiency and Stability in Evolutionary Quantum Neural Architecture Search

Linwei Shang, Hao Cao, Yang Jie Wu +2 more

Quantum neural architecture search (QNAS) has emerged as a promising approach for automatically designing parameterized quantum circuits (PQCs) for near-term quantum machine learning tasks. However, quantum evolutionary algorithm (QEA)-based QNAS methods often suffer from slow distribution concentration and insufficient update adaptivity in high-dimensional discrete search spaces, which limits both search efficiency and final model performance. To address these issues, this paper proposes Structural Probability Adaptive Quantum Neural Architecture Search (SPA-QNAS), a search-dynamics-enhanced QNAS method built upon the EQNAS benchmark framework. SPA-QNAS introduces two complementary mechanisms into the QPV-driven evolutionary search loop: Structural Probability Enhancement (SPE) and Adaptive Evolutionary Control (AEC). SPE reinforces elite structural decisions to accelerate the concentration of the structural sampling distribution toward high-fitness regions, while AEC adaptively regulates the rotation updates of non-elite individuals according to fitness feedback, thereby improving update stability and suppressing ineffective disturbances. Under the same search space, circuit template, and quantum resource budget as EQNAS, SPA-QNAS is evaluated on the MNIST and Warship benchmark datasets. Experimental results across multiple independent runs demonstrate that SPA-QNAS achieves higher classification accuracy and more stable performance compared with EQNAS. In representative experiments, SPA-QNAS achieves classification accuracies of 99.42% on MNIST and 85.33% on Warship under the same search space and quantum resource budget as EQNAS. These results indicate that improving QPV-based evolutionary update dynamics is an effective way to enhance the stability and robustness of QNAS under fixed quantum resource constraints.

Entropy•Jul 22, 2026•0 citations

Quantum Dynamic Optimization Algorithm for Neural Architecture Search on Image Classification

Jin Jin, Qian Zhang, Jia He +1 more

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.

Electronics•Nov 30, 2022•5 citations

Image Classification Using Quantum Machine Learning

Amrit Raj, Jayakumar Vaithiyashankar

Lecture notes in networks and systems•Oct 11, 2022•1 citation

Neural Architecture Search for Quantum Autoencoders

Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1 more

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.

quant-pharXiv:2511.19246•Nov 25, 2025•0 citations
Quantum neural architecture search (QNAS) has emerged as a promising approach for automatically designing parameterized quantum circuits (PQCs) for near-term quantum machine learning tasks. However, quantum evolutionary algorithm (QEA)-based QNAS methods often suffer from slow distribution concentration and insufficient update adaptivity in high-dimensional discrete search spaces, which limits both search efficiency and final model performance. To address these issues, this paper proposes Structural Probability Adaptive Quantum Neural Architecture Search (SPA-QNAS), a search-dynamics-enhanced QNAS method built upon the EQNAS benchmark framework. SPA-QNAS introduces two complementary mechanisms into the QPV-driven evolutionary search loop: Structural Probability Enhancement (SPE) and Adaptive Evolutionary Control (AEC). SPE reinforces elite structural decisions to accelerate the concentration of the structural sampling distribution toward high-fitness regions, while AEC adaptively regulates the rotation updates of non-elite individuals according to fitness feedback, thereby improving update stability and suppressing ineffective disturbances. Under the same search space, circuit template, and quantum resource budget as EQNAS, SPA-QNAS is evaluated on the MNIST and Warship benchmark datasets. Experimental results across multiple independent runs demonstrate that SPA-QNAS achieves higher classification accuracy and more stable performance compared with EQNAS. In representative experiments, SPA-QNAS achieves classification accuracies of 99.42% on MNIST and 85.33% on Warship under the same search space and quantum resource budget as EQNAS. These results indicate that improving QPV-based evolutionary update dynamics is an effective way to enhance the stability and robustness of QNAS under fixed quantum resource constraints.
Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks.
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.