Towards Scalable Quantum Unit Commitment in Power Systems: Encoding and Complexity Reduction Techniques

arXiv:2609.36999 · quant-ph, cs.SY, eess.SY · Submitted 2026-09-29 · Read on arXiv

quant-ph, cs.SY, eess.SY

Submitted: 2026-09-29

Updated: 2026-09-29

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: 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

Terminology

Adiabatic Evolution — 25× in this paper · explained in 2 episodes
Unit Commitment — 19× in this paper
Adiabatic Quantum Evolution — 9× in this paper
Pauli Correlation Encoding — 8× in this paper
Decode — 6× in this paper · explained in 2 episodes
Benchmark — 5× in this paper · explained in 2 episodes
Sample-based Quantum Diagonalisation — 4× in this paper
Hilbert Space — 3× in this paper · explained in 2 episodes
Objective Function — 3× in this paper · explained in 2 episodes
Pauli Correlation Encoding (PCE) — 3× in this paper · explained in 2 episodes
Calibration — 2× in this paper · explained in 8 episodes
Combinatorial Optimization — 2× in this paper · explained in 2 episodes
Computational overhead — 2× in this paper · explained in 2 episodes
IEEE Texas Power and Energy Conference — 2× in this paper
QUBO Formulation — 2× in this paper · explained in 3 episodes
Aggregation — 1× in this paper
Benchmarking — 1× in this paper · explained in 4 episodes
Budget-Constrained Optimization — 1× in this paper · explained in 2 episodes
Effective Hamiltonian — 1× in this paper · explained in 2 episodes
Key Performance Indicators — 1× in this paper
Physical Constraints — 1× in this paper · explained in 7 episodes
Quantum Circuits — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes

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).

Terminology

Abstract

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