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

TL;DR

This paper demonstrates an end-to-end quantum speedup for power grid optimization problems using Quantum Approximate Optimization Algorithm, supported by efficient classical simulation and experiments on realistic power grid instances.

Contribution

It extends prior theoretical quantum speedup results to an end-to-end setting, including the classical optimization component, for power grid management problems.

Findings

16 QAOA layers outperform classical baseline for up to 14 qubits.

Classical pre-computation enables large-scale quantum circuit simulation.

Quantum speedup is demonstrated in an industrially relevant power grid scenario.

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.