发表机构
Riverlane; Astex Pharmaceuticals（Riverlane公司; Astex制药公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于Lindblad模拟，针对费米子Hubbard模型基态制备，推导严格误差界并对比经验参数，发现精确过滤能量跃迁是主要成本，估计需7.7×10^8个T门。
AI 中文摘要
近期在模拟Lindblad动力学算法方面的进展已阐明了其在态制备方面的理论潜力，但其在早期容错体制及更远未来的实用性仍不确定。在本工作中，我们通过研究费米子Hubbard模型基态制备的成本来探讨这一问题，遵循[Phys. Rev. Research 6, 033147 (2024)]中的单辅助比特方法。我们推导了包含常数前置因子的严格误差界，并将其与从电路级模拟获得的经验误差行为及实际收敛参数进行比较。我们发现，经验选择的参数可将所需资源减少数个数量级，从而估计出针对36位点费米子Hubbard模型低能子空间执行一个单位时间演化需要$7.7 \ imes 10^8$个T门。我们确定能量跃迁的精确过滤是这一成本的主要来源。
英文摘要
Recent advances in algorithms for simulating Lindblad dynamics have clarified their theoretical potential for state preparation, but their practicality in the early fault-tolerant regime and beyond remains uncertain. In this work, we address this by investigating the cost of preparing ground states of the fermionic Hubbard model, following the single-ancilla approach of [Phys. Rev. Research 6, 033147 (2024)]. We derive rigorous error bounds including constant prefactors, and compare to empirical error behavior and practical convergence parameters obtained from circuit-level simulations. We find that empirically chosen parameters can reduce the required resources by orders of magnitude, resulting in an estimated $7.7 \times 10^8$ T gates to perform one unit of time evolution targeting the low-energy subspace of a 36-site fermionic Hubbard model. We identify the accurate filtering of energy transitions as the main source of this cost.
