Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding

• URL: http://arxiv.org/abs/2605.17145v1
• Date: Sat, 16 May 2026 20:30:30 GMT
• Title: Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
• Abstract summary: Unit commitment is an important optimization problem in power system operations, classified as NPhard.<n>This paper presents a hybrid quantum-classical method for the unit commitment problem with time-dependent constraints.<n>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.
• Score: 5.088908364274317
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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