A Distributed Quantum Approximate Optimization Algorithm For Unit Commitment

2026-08-04

AI Summary

This work addresses the challenge posed by the large number of binary variables in unit commitment problems, which exceeds the capacity of current single quantum devices. To overcome this limitation, the authors propose a distributed solution framework that decomposes the problem into continuous and binary subproblems. The binary subproblem is formulated as a QUBO and solved via a distributed Quantum Approximate Optimization Algorithm (DQAOA) executed collaboratively across multiple quantum processing units (QPUs), while the remaining components are handled through ADMM-based iterative updates. This approach circumvents the qubit constraints of individual quantum devices and achieves scalability without compromising solution quality relative to centralized methods. Validation on a five-generator test case with 15 binary variables demonstrates that monolithic QAOA, brute-force enumeration, and DQAOA all converge to identical scheduling decisions, power outputs, and operational costs, confirming the effectiveness and consistency of the proposed architecture.

Abstract

This paper presents a distributed quantum approximate optimization algorithm (DQAOA)-enabled three-block alternating direction method of multipliers (ADMM) framework for unit commitment (UC). The relaxed commitment and dispatch variables are solved in a continuous quadratic programming block, while the binary commitment block is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The DQAOA interface allows this QUBO to be solved using brute-force enumeration, monolithic QAOA, or distributed QAOA, while the remaining ADMM updates are kept unchanged. In the distributed mode, the logical commitment qubits are allocated across multiple capacity-constrained quantum processing units (QPU), avoiding the requirement that the complete binary problem fits on a single device. The framework is evaluated on a five-unit UC instance containing 15 binary variables. All three solver modes reduce the ADMM primal residual below a certain tolerance and recover the same commitment schedule, dispatch, and operating cost. The results demonstrate solution consistency across the three solver modes and the multi-QPU capacity accommodation provided by the distributed QAOA method.

Problem

• Unit Commitment
• Quadratic Unconstrained Binary Optimization
• Quantum Processing Unit
• Distributed Optimization
• Binary Variables

Innovation

• Distributed QAOA
• Unit Commitment
• QUBO
• ADMM
• Multi-QPU