mlsanityData to 7 Oct 2026

Challenges for Reinforcement Learning in Quantum Circuit Design

arXiv 2312.11337 ↗First posted 18 Dec 2023quant-ph cs.LGcited by 9

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Abstract (arXiv)

Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 65 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

18 of 65 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
  6. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
  7. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  8. 2021HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representationcited by 33
  9. 2022Mirror Learning: A Unifying Framework of Policy Optimisationcited by 31
  10. 2020Topological Quantum Compiling with Reinforcement Learningcited by 23
  11. 2019A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolvercited by 20
  12. 2020Quantum Circuit Design Searchcited by 13
6 more
  1. 2023Guide Your Agent with Adaptive Multimodal Rewardscited by 10
  2. 2016ProjectQ: An Open Source Software Framework for Quantum Computingcited by 6
  3. 2019Circuit Transformations for Quantum Architecturescited by 6
  4. 2023qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilationcited by 5
  5. 2022Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Explorationcited by 4
  6. 2023CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 8
  2. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
  3. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generationcited by 4
  4. 2025Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesiscited by 3
  5. 2025HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structurecited by 1
  6. 2024Automatic re-calibration of quantum devices by reinforcement learningcited by 0
  7. 2025Quantum Architecture Search for Solving Quantum Machine Learning Taskscited by 0
  8. 2026Generative Quantum Data Embeddings for Supervised Learningcited by 0
  9. 2026Approximate Quantum State Preparation Through Proximal Policy Optimizationcited by 0