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Reinforcement learning for optimization of variational quantum circuit architectures

arXiv 2103.16089 ↗First posted 30 Mar 2021quant-ph cs.AIcited by 76

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Abstract (arXiv)

The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices. However, their performance depends on the structure of the used variational ansatz, which requires balancing the depth and expressivity of the corresponding circuit. In recent years, various methods for VQE structure optimization have been introduced but the capacities of machine learning to aid with this problem has not yet been fully investigated. In this work, we propose a reinforcement learning algorithm that autonomously explores the space of possible ans"atze, identifying economic circuits which still yield accurate ground energy estimates. The algorithm is intrinsically motivated, and it incrementally improves the accuracy of the result while minimizing the circuit depth. We showcase the performance of our algorithm on the problem of estimating the ground-state energy of lithium hydride (LiH). In this well-known benchmark problem, we achieve chemical accuracy, as well as state-of-the-art results in terms of circuit depth.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 30 Mar 2021.

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16 of 61 references.

References · 16

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2020Variational Quantum Algorithmscited by 687
  3. 2019Parameterized quantum circuits as machine learning modelscited by 330
  4. 2020First return, then explorecited by 256
  5. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 141
  6. 2018Quantum generative adversarial networkscited by 128
  7. 2019A survey on intrinsic motivation in reinforcement learningcited by 79
  8. 2019Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problemscited by 30
  9. 2021Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Eracited by 22
  10. 2019A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolvercited by 20
  11. 2020Quantum Circuit Design Searchcited by 13
  12. 2020Interferobot: aligning an optical interferometer by a reinforcement learning agentcited by 12
4 more
  1. 2019Quantum Logic Gate Synthesis as a Markov Decision Processcited by 6
  2. 2018Approximation of quantum control correction scheme using deep neural networkscited by 5
  3. 2015Exploring the Quantum Speed Limit with Computer Gamescited by 2
  4. 2018Geometrical versus time-series representation of data in quantum control learningcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 6; 2023: 10; 2024: 31; 2025: 13; 2026: 12.

76 papers.

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  1. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  2. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  3. 2024Quantum Circuit Optimization with AlphaTensorcited by 36
  4. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 36
  5. 2023Quantum circuit synthesis with diffusion modelscited by 33
  6. 2024Quantum Architecture Search: A Surveycited by 31
  7. 2021QTN-VQC: An End-to-End Learning framework for Quantum Neural Networkscited by 27
  8. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
  9. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  10. 2022Concentration of Data Encoding in Parameterized Quantum Circuitscited by 15
  11. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 13
  12. 2024Meta-Designing Quantum Experiments with Language Modelscited by 12
38 more
  1. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 12
  2. 2023Enhancing variational quantum state diagonalization using reinforcement learning techniquescited by 11
  3. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  4. 2024Quantum Mixed-State Self-Attention Networkcited by 11
  5. 2022Automatic and effective discovery of quantum kernelscited by 10
  6. 2023Quarl: A Learning-Based Quantum Circuit Optimizercited by 10
  7. 2024A Reinforcement Learning Environment for Directed Quantum Circuit Synthesiscited by 10
  8. 2024Reinforcement learning-assisted quantum architecture search for variational quantum algorithmscited by 10
  9. 2024Quantum Visual Feature Encoding Revisitedcited by 10
  10. 2023Challenges for Reinforcement Learning in Quantum Circuit Designcited by 9
  11. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  12. 2024Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacitycited by 9
  13. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 8
  14. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  15. 2024Reinforcement Learning for Variational Quantum Circuits Designcited by 8
  16. 2023Improving Quantum Circuit Synthesis with Machine Learningcited by 7
  17. 2024Neural auto-designer for enhanced quantum kernelscited by 7
  18. 2024Physics-Informed Bayesian Optimization of Variational Quantum Circuitscited by 7
  19. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
  20. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  21. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture searchcited by 6
  22. 2025Synthesis of discrete-continuous quantum circuits with multimodal diffusion modelscited by 6
  23. 2021Entropy Regularized Reinforcement Learning Using Large Deviation Theorycited by 5
  24. 2025Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Searchcited by 5
  25. 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
  26. 2022AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Imagescited by 4
  27. 2024Quantum Architecture Search with Unsupervised Representation Learningcited by 4
  28. 2024Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rulecited by 4
  29. 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processorcited by 4
  30. 2024MG-Net: Learn to Customize QAOA with Circuit Depth Awarenesscited by 4
  31. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generationcited by 4
  32. 2023Toward Automated Quantum Variational Machine Learningcited by 3
  33. 2024CHARME: A chain-based reinforcement learning approach for the minor embedding problemcited by 3
  34. 2025FlowQ-Net: A Generative Framework for Automated Quantum Circuit Designcited by 3
  35. 2026Generative Quantum-inspired Kolmogorov-Arnold Eigensolvercited by 3
  36. 2024Qubit-Wise Architecture Search Method for Variational Quantum Circuitscited by 2
  37. 2025Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwarecited by 2
  38. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2

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