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Reinforcement learning optimizes quantum circuit passes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a reinforcement learning agent capable of composing sequences of quantum circuit optimization passes. This agent demonstrated a significant improvement in reducing two-qubit gates compared to default sequences, removing an average of 57.7% of gates. The approach separates the design of optimization passes from their optimal application, potentially fostering innovation in both areas. AI

IMPACT This research could lead to more efficient quantum computations by automating the optimization of quantum circuits.

RANK_REASON Academic paper detailing a novel application of reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reinforcement learning optimizes quantum circuit passes

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Academic paper detailing a novel application of reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Mills, Ifan Williams, Jacob Swain, Gabriel Matos, Enrico Rinaldi, Alexander Koziell-Pipe ·

    Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes

    arXiv:2601.21629v2 Announce Type: replace-cross Abstract: 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 e…

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