Replay-buffer engineering for noise-robust quantum circuit optimization

TL;DR

This paper introduces novel replay-buffer strategies and evaluation methods to enhance noise-robust quantum circuit optimization using deep reinforcement learning, achieving significant efficiency and accuracy improvements.

Contribution

It presents ReaPER+ for improved replay sampling, OptCRLQAS for faster evaluations, and a transfer scheme for noisy settings, advancing scalable quantum optimization techniques.

Findings

ReaPER+ achieves 4-32x sample efficiency gains.

OptCRLQAS cuts evaluation time by up to 67.5%.

Transfer scheme reduces steps to chemical accuracy by 85-90%.

Abstract

Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) targets, curriculum-based architecture search that triggers a full quantum-classical evaluation at every environment step, and the routine discard of noiseless trajectories when retraining under hardware noise. We address all three by treating the replay buffer as a primary algorithmic lever for quantum optimization. We introduce ReaPER$+$, an annealed replay rule that transitions from TD error-driven prioritization early in training to reliability-aware sampling as value estimates mature, achieving $4-32\times$ gains in sample efficiency over fixed PER, ReaPER, and uniform replay while consistently discovering more compact circuits across quantum compilation and QAS benchmarks; validation on LunarLander-v3 confirms the principle is domain-agnostic. Furthermore we eliminate the quantum-classical evaluation bottleneck in curriculum RL by introducing OptCRLQAS which amortizes expensive evaluations over multiple architectural edits, cutting wall-clock time per episode by up to $67.5\%$ on a 12-qubit optimization problem without degrading solution quality. Finally we introduce a lightweight replay-buffer transfer scheme that warm-starts noisy-setting learning by reusing noiseless trajectories, without network-weight transfer or $\epsilon$-greedy pretraining. This reduces steps to chemical accuracy by up to $85-90\%$ and final energy error by up to $90\%$ over from-scratch baselines on 6-, 8-, and 12-qubit molecular tasks. Together, these results establish that experience storage, sampling, and transfer are decisive levers for scalable, noise-robust quantum circuit optimization.