Quantum and quantum-inspired optimization for an in-core fuel management problem

TL;DR

This paper explores the application of quantum, quantum-inspired, and classical algorithms to solve the complex in-core fuel management problem in nuclear reactors, demonstrating potential for advanced computational methods in energy industry optimization.

Contribution

It reformulates the nuclear fuel reloading problem as a QUBO and benchmarks quantum, quantum-inspired, and classical algorithms on realistic reactor configurations.

Findings

Quantum and quantum-inspired algorithms show promising results.

Benchmarking demonstrates potential advantages over classical methods.

The approach enables efficient exploration of large search spaces.

Abstract

Operation management of nuclear power plants consists of several computationally hard problems. Searching for an in-core fuel loading pattern is among them. The main challenge of this combinatorial optimization problem is the exponential growth of the search space with a number of loading elements. Here we study a reloading problem in a Quadratic Unconstrained Binary Optimization (QUBO) form. Such a form allows us to apply various techniques, including quantum annealing, classical simulated annealing, and quantum-inspired algorithms in order to find fuel reloading patterns for several realistic configurations of nuclear reactors. We present the results of benchmarking the in-core fuel management problem in the QUBO form using the aforementioned computational techniques. This work demonstrates potential applications of quantum computers and quantum-inspired algorithms in the energy industry.