QSim-Arena：面向量子模拟优势评估的演化式 AI 开发者框架

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Abstract:Where practical advantage emerges in quantum dynamics simulation remains a central debate in quantum computing. Claimed advantages can be rapidly narrowed or overturned by stronger classical methods, while progress on both sides can outpace the journal publication cycle. We present QSim-Arena, a research framework in which AI agents combine a closed loop of verification and benchmarking with open exploration. Hamiltonians, initial states and accuracy targets for evolved states or observable expectation values provide explicit specifications for evaluating simulation methods. Agents translate published techniques into executable components, reproduce reported results and benchmark their performance across diverse Hamiltonian families. Competing classical methods are implemented and independently crosschecked, providing credible baselines for end-to-end quantum resource estimates based on rigorous error bounds and empirical calibration. Alongside this closed loop, agents recombine classical simulation techniques and co-design fault-tolerant quantum circuits using knowledge extracted from the literature. Initial applications improve baseline resource estimates through the integration of established techniques. QSim-Arena thus treats quantum advantage assessment as an evolving, reproducible research process that can respond to advances in algorithms, error analysis and hardware.

Submission history

From: Jue Xu [view email]
[v1] Wed, 11 Nov 2026 03:58:54 UTC (346 KB)