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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
GenQAS is a reinforcement learning framework that uses learned generative replay to improve quantum circuit discovery in simulation across benchmarks from 6 to 15 qubits. The method shows substantial gains in success probability relative to passive replay, but has not been validated on real quantum hardware and has not undergone peer review.
Computational algorithm comparison study. Quantum circuit optimization tasks on chemical Hamiltonian systems and Ising models in 6–15 qubit regimes, including a noisy simulation variant.. Intervention: GenQAS: reinforcement learning with learned generative replay and prioritized experience mixing. Compared with: Passive replay (baseline RL without generative replay).
At 12 qubits, GenQAS improves final success probability by up to 7.0× over passive replay On a 15-qubit transverse field Ising model, GenQAS increases success probability from 12% to 21% In a noisy 6-qubit BeH₂ transfer experiment, generative replay reduces steps to chemical accuracy by 92.7%
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This is a computational methods paper with no experimental validation on real quantum hardware, presenting an algorithm improvement in simulation only; the work is sound but confined to modelling and needs real-world testing.
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Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search space. Existing replay mechanisms reuse observed transitions; the proposed learned model produces additional predicted one step transitions from real state-action seeds. Here we introduce GenQAS, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay. A learned local transition model generates synthetic circuit transitions on demand and mixes them with real experience during Double Deep Q-Network updates. Under a random exploration analysis, near ground state circuits occupy a rapidly shrinking region of the accessible state space. We investigate whether real data anchored synthetic replay can improve the effective training signal in this regime. Across chemical Hamiltonian benchmarks from 6 to 12 qubits, GenQAS improves fixed-budget success probability and identifies compact circuits at competitive energy error. At 12 qubits, it improves final success probability by up to $7.0\times$ over passive replay. On a 15-qubit transverse field Ising model, GenQAS increases success probability from $12\%$ to $21\%$. In a noisy 6-qubit BeH$_2$ transfer experiment, generative replay reduces the steps to chemical accuracy by $92.7\%$. These results show that generative replay can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.
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