End-to-End Speedup for Quantum Simulation-Based Optimization in Power Grid Management

Abstract

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.