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Paper 1
Geometry-induced correlated noise in qLDPC syndrome extraction
Angelo Di Bella
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2604.01040
- arXiv
- 2604.01040
With code and syndrome-extraction schedule fixed, can routed geometry alone change the correlated fault model enough to impact logical performance? Starting from a geometry-conditioned same-tick interaction Hamiltonian, we derive a controlled retained single-and-pair data-fault model for bivariate-bicycle (BB) layouts. Two geometry metrics emerge in two kernel regimes: under a crossing-local diagnostic kernel, a matching argument reduces the support-level effective fault weight; when every support pair appears in at least one retained round with finite same-round separation, strictly positive kernels saturate the support graph, and weighted exposure becomes the discriminating quantity. Circuit-level Monte Carlo on the $[\![72, 12, 6]\!]$ and $[\![144, 12, 12]\!]$ benchmarks confirms that a biplanar bounded-thickness layout suppresses the monomial single-layer embedding penalty, with weighted exposure tracking logical error rate across 101 operating points (Spearman correlation 0.893). A single-layer logical-family optimization on BB72 reduces worst-case exposure by 26.11% and lowers logical error rate in the tested power-law window. Routed geometry should be optimized together with code, schedule, and decoder.
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QHap: Quantum-Inspired Haplotype Phasing
Rui Zhang, Xian-Zhe Tao, Yibo Chen, Jiawei Zhang, Lei He, Dongming Fang, Lin Yang, Yuhui Sun, Qinyuan Zheng, Xinmeng Shi, Yang Zhou, Wanyi Chen, Chentao Yang, Man-Hong Yung, Jun-Han Huang
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.25762
- arXiv
- 2603.25762
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 tool that leverages quantum-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 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 chromatin conformation capture data extends phase block contiguity by up to 15-fold, enabling near-chromosome-spanning 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.
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