发表机构
Universitat Politècnica de Catalunya; CITCEA-UPC（加泰罗尼亚理工大学; 加泰罗尼亚理工大学能源与环境工程研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结合泡利关联编码与基于样本的量子对角化的量子机组组合框架，通过减少量子比特和增强最优解概率，实现比未简化方法更可靠且计算成本更低的求解。
AI 中文摘要
本文开发了一个量子优化机组组合（UC）框架，该框架受益于泡利关联编码（PCE）作为量子比特减少的预处理技术，以及基于样本的量子对角化（SQD）作为混合后处理细化方法。UC问题以二进制形式表述，然后转化为QUBO问题，并映射到适合在Qibo框架内进行绝热量子演化的Ising表示上。本研究分析了随着发电机数量的增加，PCE和SQD技术如何影响量子比特可扩展性、计算工作量、可行性以及恢复最优解的概率。结果表明，PCE减少了编码问题的有效规模，SQD提高了可行且最优解上的概率集中度，并且它们的组合应用在几个可扩展案例研究中提供了最佳的整体行为，以比未简化表述更低的有效计算成本实现了更可靠的解决方案。该代码库在Github上开源提供（链接在论文发表前尚不可用；然而，如果审稿人要求，我们可以在特定的时间窗口内将其开放）。
英文摘要
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).
