Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

TL;DR

This paper proposes a hybrid quantum-classical algorithm based on QAOA for solving the power system Unit Commitment problem, demonstrating potential advantages for large-scale instances in the NISQ era.

Contribution

It extends QAOA with a classical minimizer to handle mixed binary optimization in power system scheduling, a novel application in this domain.

Findings

Classical solvers are effective for small instances under 400 units.

Quantum advantage may emerge for larger instances with hundreds of units.

Simulations validate the hybrid approach's potential for large-scale power optimization.

Abstract

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. Potential quantum advantage would require problem instances at this scale, with several hundred units.