Neural Architecture Search for Quantum Autoencoders

• URL: http://arxiv.org/abs/2511.19246v1
• Date: Mon, 24 Nov 2025 15:55:44 GMT
• Title: Neural Architecture Search for Quantum Autoencoders
• Authors: Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Shinjae Yoo,
• Abstract summary: We propose a neural architecture search (NAS) framework that automates the design of quantum autoencoders.<n>By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders.
• Score: 30.784540105806784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning [11.861417859173859]
  Quantum Principal Geodesic Analysis (qPGA) is a non-invertible method for dimensionality reduction and qubit-efficient encoding.<n>We show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders.<n>In downstream QML classification tasks, qPGA can achieve over 99% accuracy and F1-score on MNIST and Fashion-MNIST, outperforming quantum-dependent baselines.
  arXiv  Detail & Related papers  (2025-06-24T03:09:16Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation [4.358861563008207]
  Quantum neural networks (QNNs) have shown promise both empirically and theoretically.<n> Hardware imperfections and limited access to quantum devices pose practical challenges.<n>We propose an automated solution using differentiable optimization.
  arXiv  Detail & Related papers  (2025-05-13T19:01:08Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• Quantum autoencoders for image classification [0.0]
  Quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning.<n>This study introduces a novel image-classification approach using QAEs, achieving classification without requiring additional qubits.
  arXiv  Detail & Related papers  (2025-02-21T07:13:38Z)
• Quantum Data Encoding and Variational Algorithms: A Framework for Hybrid Quantum Classical Machine Learning [0.0]
  Quantum Machine Learning (QML) integrates the calculational framework of quantum mechanics with the adaptive properties of classical machine learning.<n>This article suggests a broad architecture that allows the connection between classical data pipelines and quantum algorithms.
  arXiv  Detail & Related papers  (2025-02-17T16:04:04Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.