Generative Replay Method Reduces Sample Starvation in Quantum Architecture Search

Reinforcement learning can automate the design of quantum circuits, but performance degrades as the search space grows and rewarding circuit trajectories become rare. This arXiv paper proposes a generative replay technique that synthesizes new training samples instead of only reusing previously observed transitions, addressing the resulting data shortage. The authors report that this approach improves the scalability of quantum architecture search.