Replay-buffer engineering for noise-robust quantum circuit optimization
This paper introduces ReaPER+, OptCRLQAS, and a lightweight transfer scheme to overcome key bottlenecks in deep reinforcement learning for quantum circuit optimization by engineering replay buffers for noise robustness, amortizing expensive evaluations, and reusing noiseless trajectories, thereby achieving significant gains in sample efficiency, wall-clock time, and solution accuracy across various quantum benchmarks.