mlsanityData to 14 Oct 2026

Automated Quantum Circuit Design with Nested Monte Carlo Tree Search

arXiv 2207.00132 ↗First posted 1 Jul 2022quant-ph cs.ET cs.LGcited by 16

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Abstract (arXiv)

Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years. Despite the adaptability and simplicity, their scalability and the selection of suitable ans"atzs remain key challenges. In this work, we report an algorithmic framework based on nested Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed bandit (CMAB) model for the automated design of quantum circuits. Through numerical experiments, we demonstrated our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimisation on a graph, solving systems of linear equations, and finding encoding circuit for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimise quantum circuits for larger systems, showing both versatility and scalability.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 9 of its 52 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

9 of 52 references.

References · 9

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  2. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  3. 2017Progressive Neural Architecture Searchcited by 487
  4. 2020Quantum circuit architecture search for variational quantum algorithmscited by 82
  5. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
  6. 2019Understanding Architectures Learnt by Cell-based Neural Architecture Searchcited by 48
  7. 2020Theory-Inspired Path-Regularized Differential Network Architecture Searchcited by 25
  8. 2018Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Searchcited by 18
  9. 2021Neural Architecture Search via Combinatorial Multi-Armed Banditcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 5; 2025: 5; 2026: 5.

16 papers.

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  1. 2024Quantum Architecture Search: A Surveycited by 32
  2. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 14
  3. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 13
  4. 2025Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Searchcited by 5
  5. 2024Qubit-Wise Architecture Search Method for Variational Quantum Circuitscited by 2
  6. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2
  7. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Componentscited by 2
  8. 2026How to find expressible and trainable parameterized quantum circuits?cited by 2
  9. 2022Decoding surface codes with deep reinforcement learning and probabilistic policy reusecited by 1
  10. 2025Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correctioncited by 1
  11. 2026Replay-buffer engineering for noise-aware quantum circuit optimizationcited by 1
  12. 2024Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Termscited by 0
4 more
  1. 2025Quantum Architecture Search for Solving Quantum Machine Learning Taskscited by 0
  2. 2026Magic-Informed Quantum Architecture Searchcited by 0
  3. 2026Zero-shot Quantum Neural Architecture Searchcited by 0
  4. 2026Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Searchcited by 0