一种用于染色体Y系统发育重建的块分解QUBO工作流
A Block Decomposed QUBO Workflow for Chromosome-Y Phylogeny Reconstruction
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中文总结 AI 辅助
本文提出一种结合QUBO公式与ADMM分解及DCQO量子求解器的工作流，从VCF文件重建Y染色体系统发育，实现拓扑与根位置优化，可扩展至超越单量子处理器比特预算的规模。
中文摘要 AI 辅助
本文提出了一种计算工作流，从双等位基因单核苷酸多态性（SNP）的变异调用格式（VCF）文件中重建人类Y染色体群体的系统发育。两个经典的系统发育决策——拓扑选择和根位置确定——被表述为二次无约束二元优化（QUBO）问题。该工作流将两种QUBO公式与交替方向乘子法（ADMM）分解策略以及插件式数字化反绝热量子优化（DCQO）求解器相结合，使得大型系统发育优化问题能够在多个经典或量子计算资源上执行。DCQO基于门电路的优化器在脉冲机制下使用短深度反绝热电路驱动每个ADMM块QUBO，无需外部变分循环。ADMM分解与DCQO块求解器的结合使得问题规模能够超越任何单个数字量子处理单元调用的量子比特预算，同时保持原始目标的完整性。该工作流从VCF文件中提取基因型信息，通过两种QUBO公式重建拓扑和根位置，并使用PhyloTree对所得树进行注释。最终数据集包含一个Nexus注释的有根树以及诊断图。该工作流展示了适度规模的QUBO公式与ADMM分解相结合，在群体基因组学领域作为贪心启发式算法的可扩展替代方案的潜力。
英文摘要
This paper sets out a computational workflow that reconstructs the phylogeny of human Y-chromosome populations from a Variant Call Format (VCF) file of biallelic Single Nucleotide Polymorphisms (SNP). The two classical phylogenetic decisions - topology selection and root placement - are cast as Quadratic Unconstrained Binary Optimisation (QUBO) problems. The workflow combines two QUBO formulations with an Alternating Direction Method of Multipliers (ADMM) decomposition strategy and a plug-in Digitized Counter-Diabatic Quantum Optimization (DCQO) solver, enabling large phylogenetic optimization problems to be executed across multiple classical or quantum computing resources. The DCQO gate-based optimizer drives each ADMM-block QUBO with short-depth counter-diabatic circuits in the impulse regime, without the necessity for an outer variational loop. The combination of ADMM decomposition and the DCQO block solver facilitates problem sizes that surpass the qubit budget of any individual digital quantum processing unit call, while maintaining the integrity of the original objective. The workflow extracts genotype information from VCF files, reconstructs topology and rooting through two QUBO formulations, and annotates the resulting tree using PhyloTree. The resultant data set comprises a Nexus-annotated rooted tree, in addition to diagnostic figures. The workflow demonstrates the potential of modest-scale QUBO formulations combined with ADMM decomposition to serve as a scalable alternative to greedy heuristics in the domain of population genomics.
发表机构
- CRS4 - Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna（撒丁岛研究、发展与高等研究中心）
机构由 AI 辅助整理，请以论文原文为准。
