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AI Methods for Permutation Circuit Synthesis Across Generic Topologies

arXiv 2509.16020 ↗First posted 19 Sep 2025quant-ph cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper investigates artificial intelligence (AI) methodologies for the synthesis and transpilation of permutation circuits across generic topologies. Our approach uses Reinforcement Learning (RL) techniques to achieve near-optimal synthesis of permutation circuits up to 25 qubits. Rather than developing specialized models for individual topologies, we train a foundational model on a generic rectangular lattice, and employ masking mechanisms to dynamically select subsets of topologies during the synthesis. This enables the synthesis of permutation circuits on any topology that can be embedded within the rectangular lattice, without the need to re-train the model. In this paper we show results for 5x5 lattice and compare them to previous AI topology-oriented models and classical methods, showing that they outperform classical heuristics, and match previous specialized AI models, and performs synthesis even for topologies that were not seen during training. We further show that the model can be fine tuned to strengthen the performance for selected topologies of interest. This methodology allows a single trained model to efficiently synthesize circuits across diverse topologies, allowing its practical integration into transpilation workflows.

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References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  3. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
  4. 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 161
  5. 2018Universal Quantum Control through Deep Reinforcement Learningcited by 44
  6. 2019Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computerscited by 22
  7. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learningcited by 22
  8. 2019Circuit Transformations for Quantum Architecturescited by 6
  9. 2022Improving Quantum Computation by Optimized Qubit Routingcited by 3
  10. 2025Pauli Network Circuit Synthesis with Reinforcement Learningcited by 2

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