Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms

TL;DR

This paper explores adapting quantum algorithms like QA and QAOA to solve robust optimization problems in energy management, demonstrating their potential to produce more reliable solutions in uncertain, complex scenarios.

Contribution

It introduces two novel heuristics that leverage quantum stochasticity for robust solutions and applies them to energy sector problems, advancing quantum optimization in practical applications.

Findings

Quantum heuristics improve solution robustness in energy problems.

Application to unit commitment and EV charging demonstrates practical relevance.

Quantum methods show potential for more reliable decision-making in uncertain environments.

Abstract

In the rapidly advancing domain of quantum optimization, the confluence of quantum algorithms such as Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) with robust optimization methodologies presents a cutting-edge frontier. Although it seems natural to apply quantum algorithms when facing uncertainty, this has barely been approached. In this paper we adapt the aforementioned quantum optimization techniques to tackle robust optimization problems. By leveraging the inherent stochasticity of quantum annealing and adjusting the parameters and evaluation functions within QAOA, we present two innovative methods for obtaining robust optimal solutions. These heuristics are applied on two use cases within the energy sector: the unit commitment problem, which is central to the scheduling of power plant operations, and the optimization of charging electric vehicles (EVs) including electricity from photovoltaic (PV) to minimize costs. These examples highlight not only the potential of quantum optimization methods to enhance decision-making in energy management but also the practical relevance of the young field of quantum computing in general. Through careful adaptation of quantum algorithms, we lay the foundation for exploring ways to achieve more reliable and efficient solutions in complex optimization scenarios that occur in the real-world.