mlsanityData to 14 Oct 2026

Neural Architecture Search Algorithms for Quantum Autoencoders

arXiv 2509.15451 ↗First posted 18 Sep 2025quant-ph cs.LGcited by 1

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Abstract (arXiv)

The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this approach cannot scale to more complex quantum algorithms in the future without exponentially increasing the circuit design effort and introducing unwanted inductive biases. Motivated by this observation, we propose to automate the process of cicuit design by drawing inspiration from Neural Architecture Search (NAS). In this work, we propose two Quantum-NAS algorithms that aim to find efficient circuits given a particular quantum task. We choose quantum data compression as our driver quantum task and demonstrate the performance of our algorithms by finding efficient autoencoder designs that outperform baselines on three different tasks - quantum data denoising, classical data compression and pure quantum data compression. Our results indicate that quantum NAS algorithms can significantly alleviate the manual effort while delivering performant quantum circuits for any given task.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

25 of 53 references.

References · 25

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13 more
  1. 2021Automatic design of quantum feature mapscited by 28
  2. 2019A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolvercited by 20
  3. 2022BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithmscited by 16
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  9. 2021Quantum Embedding Search for Quantum Machine Learningcited by 7
  10. 2025QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networkscited by 7
  11. 2023Learning To Optimize Quantum Neural Network Without Gradientscited by 4
  12. 2023Benchmarking Machine Learning Models for Quantum Error Correctioncited by 2
  13. 2023QArchSearch: A Scalable Quantum Architecture Search Packagecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencodercited by 0