mlsanityData to 14 Oct 2026

Neural Architecture Search for Quantum Autoencoders

arXiv 2511.19246 ↗First posted 24 Nov 2025quant-ph cs.AI cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 37 of its 71 references that are in the map. First posted on arXiv 24 Nov 2025.

Builds on

37 of 71 references.

References · 37

  1. 2020The power of quantum neural networkscited by 379
  2. 2021Generalization in quantum machine learning from few training datacited by 216
  3. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  4. 2020Quantum Long Short-Term Memorycited by 120
  5. 2019Transfer learning in hybrid classical-quantum neural networkscited by 114
  6. 2017Spurious Local Minima are Common in Two-Layer ReLU Neural Networkscited by 104
  7. 2017Quantum machine learning: a classical perspectivecited by 95
  8. 2021Parametrized quantum policies for reinforcement learningcited by 90
  9. 2020Quantum Convolutional Neural Networks for High Energy Physics Data Analysiscited by 64
  10. 2021The Dawn of Quantum Natural Language Processingcited by 61
  11. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
  12. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 51
25 more
  1. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  2. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  3. 2015Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selectioncited by 39
  4. 2024Quantum Architecture Search: A Surveycited by 32
  5. 2021QTN-VQC: An End-to-End Learning framework for Quantum Neural Networkscited by 27
  6. 2021Machine learning and deep learningcited by 26
  7. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  8. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  9. 2022Quantum deep recurrent reinforcement learningcited by 24
  10. 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 24
  11. 2023Applying QNLP to sentiment analysis in financecited by 23
  12. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  13. 2020Advances in Quantum Deep Learning: An Overviewcited by 21
  14. 2023Machine Learning for Practical Quantum Error Mitigationcited by 20
  15. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  16. 2025Learning to Measure Quantum Neural Networkscited by 15
  17. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 14
  18. 2023Differentiable Quantum Architecture Search for Quantum Reinforcement Learningcited by 13
  19. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 13
  20. 2024Quantum Gradient Class Activation Map for Model Interpretabilitycited by 9
  21. 2024Reinforcement Learning for Variational Quantum Circuits Designcited by 9
  22. 2024Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacitycited by 9
  23. 2021On exploring the potential of quantum auto-encoder for learning quantum systemscited by 6
  24. 2023Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysiscited by 3
  25. 2024A Hybrid Quantum-Classical Autoencoder Framework for End-to-End Communication Systemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1