mlsanityData to 7 Oct 2026

Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search

arXiv 2104.01992 ↗First posted 1 Apr 2021quant-ph cs.LGcited by 9

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Abstract (arXiv)

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. We propose a procedure for qubit routing that is architecture agnostic and that outperforms other available routing implementations on various circuit benchmarks. The depth of the transformed quantum circuits is minimised by utilizing the Monte Carlo tree search to perform qubit routing, aided by a Graph neural network that evaluates the value function and action probabilities for each state.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

10 of 37 references.

References · 10

  1. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  2. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  3. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 235
  4. 2018Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimizationcited by 25
  5. 2019Solving NP-Hard Problems on Graphs with Extended AlphaGo Zerocited by 23
  6. 2019On the qubit routing problemcited by 11
  7. 2019Circuit Transformations for Quantum Architecturescited by 6
  8. 2019Learning Self-Game-Play Agents for Combinatorial Optimization Problemscited by 6
  9. 2019Optimization of Quantum Circuit Mapping using Gate Transformation and Commutationcited by 6
  10. 2018Monte Carlo Tree Search for Asymmetric Treescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  2. 2023Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applicationscited by 16
  3. 2023Quarl: A Learning-Based Quantum Circuit Optimizercited by 10
  4. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  5. 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Searchcited by 8
  6. 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
  7. 2023Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In during Sparse Matrix Decompositioncited by 4
  8. 2025Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Modelscited by 0
  9. 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learningcited by 0