mlsanityData to 7 Oct 2026

Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes

arXiv 2601.21629 ↗First posted 29 Jan 2026quant-ph cs.LGcited by 0

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Abstract (arXiv)

Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which they are applied is left to the user, or else defined in general-purpose default pass sequences. While general-purpose sequences miss opportunities for optimisation which are particular to individual circuits, designing pass sequences bespoke to particular circuits requires exceptional knowledge about quantum circuit design and optimisation. Here we propose and demonstrate training a reinforcement learning agent to compose optimisation-pass sequences. In particular the agent's action space consists of passes for two-qubit gate count reduction used in default PyTKET pass sequences. For the circuits in our diverse test set, the (mean, median) fraction of two-qubit gates removed by the agent is (57.7%, 56.7 %), compared to (41.8 %, 50.0 %) for the next best default pass sequence.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 69 references that are in the map. First posted on arXiv 29 Jan 2026.

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References · 19

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7 more
  1. 2019A Verified Optimizer for Quantum Circuitscited by 7
  2. 2023Improving Quantum Circuit Synthesis with Machine Learningcited by 7
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