QHap: Quantum-Inspired Haplotype Phasing

TL;DR

QHap is a quantum-inspired haplotype phasing algorithm that significantly accelerates processing while maintaining accuracy, enabling near-chromosome-scale reconstructions and addressing scalability in genomics.

Contribution

It introduces a GPU-accelerated quantum-annealing-inspired method reformulated as Max-Cut for efficient, large-scale haplotype phasing in genomics.

Findings

QHap achieves 4-20x faster phasing than HapCUT2 and WhatsHap.

Zero switch error across multiple long-read platforms.

Integration with Pore-C data increases haplotype N50 up to 15-fold.

Abstract

Haplotype phasing, the process of resolving parental allele inheritance patterns in diploid genomes, is critical for precision medicine and population genetics, yet the underlying optimization is NP-hard, posing a scalability challenge. To address this, we introduce QHap, a haplotype phasing algorithm that leverages quantum-annealing-inspired optimization. By reformulating haplotype phasing as a Max-Cut problem and deploying a GPU-accelerated ballistic simulated bifurcation solver, QHap accelerates phasing while maintaining accuracy comparable to established phasing tools. On the highly polymorphic human major histocompatibility complex region, QHap demonstrates 4- to 20-fold acceleration over HapCUT2 and WhatsHap with zero switch error across multiple long-read sequencing platforms. The framework implements two strategies: a read-based method for regional phasing, and a single nucleotide polymorphism-based method that, through quality-weighted probabilistic edge construction, efficiently scales to chromosome-scale tasks. Integration of Pore-C chromatin conformation capture data increases the haplotype N50 by up to 15-fold, enabling near-chromosome-scale haplotype reconstruction. QHap demonstrates that quantum-inspired algorithms operating on classical hardware offer a promising approach to addressing the growing computational demands of sequencing data, establishing a new paradigm for applying physics-inspired optimization to fundamental challenges in computational genomics.