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This research presents a scalable quantum optimization framework for power system unit commitment, utilizing Pauli Correlation Encoding to reduce qubit requirements and Sample-based Quantum Diagonalisation to refine solution probabilities. The study demonstrates that combining these techniques yields more reliable optimal solutions with lower computational costs compared to unreduced formulations as the number of generators increases. This work advances the practical application of adiabatic quantum evolution for complex energy grid management problems.
Towards Scalable Quantum Unit Commitment in Power Systems: Encoding and Complexity Reduction Techniques
arXiv:2609.36999v1 Announce Type: new Abstract: This paper develops a Quantum optimisation Unit Commitment (UC) framework that benefits from Pauli Correlation Encoding (PCE) as a qubit reduction preprocessing technique and Sample-based Quantum Diagonalisation (SQD) as a hybrid post-processing refinement method. The UC problem is formulated in binary form, then transformed into a QUBO problem and mapped onto an Ising representation suitable for adiabatic quantum evolution within the Qibo framework. This study analyses how PCE and SQD techniques affect qubit scalability, computational effort, feasibility, and probability of recovering the optimal solution as the number of generators increases. The results show that PCE reduces the effective size of the encoded problem, SQD improves the concentration of probability on feasible and optimal solutions, and their combined application provides the best overall behaviour in several scalable case studies, achieving more reliable solutions with lower effective computational cost than the unreduced formulation. The repository is available open source in Github (link is not yet available until work is published. However, if requested by the reviewers, we can make it open for a scific time window, if requested).
Abstract:This paper develops a Quantum optimisation Unit Commitment (UC) framework that benefits from Pauli Correlation Encoding (PCE) as a qubit reduction preprocessing technique and Sample-based Quantum Diagonalisation (SQD) as a hybrid post-processing refinement method. The UC problem is formulated in binary form, then transformed into a QUBO problem and mapped onto an Ising representation suitable for adiabatic quantum evolution within the Qibo framework. This study analyses how PCE and SQD techniques affect qubit scalability, computational effort, feasibility, and probability of recovering the optimal solution as the number of generators increases. The results show that PCE reduces the effective size of the encoded problem, SQD improves the concentration of probability on feasible and optimal solutions, and their combined application provides the best overall behaviour in several scalable case studies, achieving more reliable solutions with lower effective computational cost than the unreduced formulation. The repository is available open source in Github (link is not yet available until work is published. However, if requested by the reviewers, we can make it open for a scific time window, if requested).
Quantum Physics (quant-ph); Systems and Control (eess.SY)
arXiv:2609.36999 [quant-ph]
(or arXiv:2609.36999v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.36999 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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