Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or constraints that depend on summary statistic information about the simulation of a physical system or process. This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver for the unit commitment problem to an end-to-end speedup, explicitly including the outer optimization component. The numerical experiments were conducted using randomly generated power grid instances of varying sizes and loads that adhere to the physical properties of real world power grids. Exploiting clever classical pre-computation, we develop a very efficient classical quantum circuit simulation that bypasses costly ancillary qubit requirements of the original algorithm, allowing for large-scale experiments. We show that 16 QAOA layers suffice to outperform a strong classical baseline for problems involving up to 14 qubits in scenarios of high load and perform on par otherwise. In summary, our results thus extend previous partial quantum speedup results for QuSO problems to an end-to-end setting that encompasses the runtime of the complete algorithm for a problem of industrial relevance.

翻译：量子仿真优化（QuSO）是一类近期提出的优化问题，其目标函数或约束条件依赖于物理系统或过程仿真的汇总统计信息，具有工业应用价值。本研究将现有理论成果（基于QAOA的QuSO求解器在机组组合问题的仿真组件上实现指数级加速）推广至端到端加速框架，明确包含外层优化组件。数值实验采用符合真实电网物理特性的随机生成电网实例（不同规模与负载级别），通过利用巧妙的经典预计算技术，开发出高效的经典量子线路仿真方法，绕过了原始算法对昂贵辅助量子比特的需求，从而支持大规模实验。结果表明：在高负载场景下，使用16层QAOA即可在涉及最多14量子比特的问题中超越强经典基线，其余场景性能相当。综上，本研究将先前QuSO问题的部分量子加速结果扩展至端到端场景，完整涵盖了具有工业相关性的算法运行时间。