Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding

TL;DR

This paper introduces a hybrid quantum-classical approach for large-scale unit commitment problems in power systems, using Pauli-Correlation Encoding to reduce qubit requirements and improve scalability.

Contribution

The paper proposes a novel Pauli-Correlation Encoding technique that enables scalable quantum optimization for multi-period unit commitment with fewer qubits.

Findings

Successfully scaled to 312 binary variables

Produced feasible schedules with competitive costs

Handled multi-period constraints effectively

Abstract

Unit commitment is an important optimization problem in power system operations, classified as NP-hard. This paper presents a hybrid quantum-classical method for the unit commitment problem with time-dependent constraints, where decisions must be made about which generators to turn on/off and how much power they should produce over a planning horizon. We use a hybrid quantum-classical optimization procedure to determine the on/off schedules of the generating units and the corresponding power dispatch that satisfies operational constraints such as load balance, generator limits, ramping, and reserve requirements. We frame the optimization loop as a leader-follower structure, where the quantum optimizer leads to give the on/off decisions, and the classical optimizer follows to produce the power level schedule. Leveraging Pauli-Correlation Encoding, our method scales to horizon-wide unit commitment schedules by encoding the binary variables with far fewer qubits. By combining these components, the method can handle multi-period settings while using far fewer qubits than straightforward quantum encodings that allocate one qubit per decision variable as in prior approaches. We evaluate the approach on both small- and large-scale instances, up to 312 binary variables, and show that it reliably produces feasible schedules with competitive operating costs.