Quantum Annealing Continuous Optimisation in Renewable Energy

TL;DR

This paper introduces QuAnCO, a novel quantum annealing-based optimization method for continuous problems in renewable energy, transforming them into QUBO form to leverage quantum annealers like D-Wave.

Contribution

The paper presents a new Trust Region-based algorithm that converts continuous optimization problems into QUBO format suitable for quantum annealing, demonstrated on a real-world biomass optimization case.

Findings

Feasibility of using quantum annealing for continuous energy optimization.

Performance advantages over classical methods in biomass mix selection.

Successful application to a large-scale biogas production problem.

Abstract

Renewable energy optimisation poses computationally-intensive challenges. Yet, often the continuous nature of the decision space precludes the use of many emerging, non-von-Neumann computing platforms such as quantum annealing, which are limited to discrete problems. We propose Quantum Annealing Continuous Optimisation (QuAnCO), a Trust Region (TR)-based algorithm, where the TR Newton sub-problem is transformed into Quadratic Unconstrained Binary Optimisation (QUBO), thereby allowing the use of Ising solvers such as D-Wave's quantum annealer. This transformation to QUBO is done by 1) using a hyper-rectangular shape for the TR, 2) discrete representation of each continuous dimension using an interval-bounded integer, and 3) binary encoding of the resulting bounded integers. We tackle a real-world challenge of optimising the biomass mix selection for Nature Energy, the largest biogas producer in Europe, thus providing evidence of feasibility and performance advantage in using QuAnCO in green energy production, and beyond.