🤖 AI Summary
This study addresses the computational challenges of topology selection and rooting in reconstructing the human Y-chromosome phylogeny by proposing a novel framework that integrates quantum-inspired optimization. Methodologically, phylogenetic decision-making is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, where the Alternating Direction Method of Multipliers (ADMM) enables modular decomposition to overcome qubit budget constraints, and a Divide-and-Conquer QUBO Optimization (DCQO) solver facilitates efficient optimization. The results demonstrate that, when applied to VCF data parsing, this approach generates annotated rooted trees alongside diagnostic visualizations. It significantly outperforms conventional greedy heuristic algorithms, thereby validating its scalability and application potential for large-scale phylogenetic analyses in population genomics.
📝 Abstract
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.