Quantum Resource Estimation for Minimising Energy Grid Losses

TL;DR

This paper investigates using gate-based quantum computing to optimize distribution network configurations, aiming to reduce power losses in real-world electricity grids by formulating the problem as a HUBO and estimating quantum resources needed.

Contribution

It introduces a novel HUBO formulation for DNR, applies it to real MV networks, and performs quantum resource estimation to evaluate future feasibility.

Findings

Quantum resource requirements depend on network structure and size.

HUBO formulation reduces qubit requirements by avoiding auxiliary variables.

Application to real MV network demonstrates practical relevance.

Abstract

Distribution network reconfiguration (DNR) can minimise power losses by identifying the optimal topology of the electricity grid. Determining the minimum loss configuration is NP-hard, and classical optimisation methods struggle to scale to real-world distribution grids. This paper explores the use of gate-based quantum computing to solve DNR for power loss reduction. We formulate DNR as a higher-order unconstrained binary optimisation (HUBO) problem, avoiding the need for auxiliary variables, thereby reducing the required number of qubits. This is applied to a real medium voltage (MV) network operated by Alliander, a Dutch distribution system operator (DSO). For each biconnected component in the network graph, we construct the corresponding HUBO, derive the cost and mixer operators, and determine the number of required logical qubits and rotation gates. These are then mapped to physical qubits and execution time estimates using quantum resource estimation (QRE). The results suggest that the quantum resource requirements depend not only on component size but also on structural characteristics such as connectivity and cyclicity. Overall, the novelty of this work lies in directly framing the optimisation problem as a HUBO, applying it to real-world MV networks, and performing a QRE to assess future feasibility.