arXiv subjects

Jun-Han Huang

Publications and source records attributed to Jun-Han Huang.

8 recordsLinked to original sources

arXiv2026

QHap: Quantum-Inspired Haplotype Phasing

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.

q-bio.GN↗

arXiv2025

Enhanced Distributed Variational Quantum Eigensolver for Large-Scale MaxCut Problem

MaxCut is a canonical NP-hard combinatorial optimization problem in graph theory with broad applications ranging from physics to bioinformatics. Although variational quantum algorithms offer promising new approaches that may eventually outperform classical schemes, they suffer from resource constraints and trainability issues such as barren plateaus, making large-scale instances intractable on noisy intermediate-scale quantum devices. In this paper, we propose an enhanced distributed variational quantum eigensolver for large-scale MaxCut problems, which extends our prior distributed variational quantum eigensolver framework by integrating a novel hybrid classical-quantum perturbation strategy, enhances optimization scalability and efficiency. Our algorithm solves weighted MaxCut instances with up to 1000 vertices using only 10 qubits, and numerical results indicate that it consistently outperforms the Goemans-Williamson algorithm. We further employ a warm-start initialization strategy, seeding the algorithm with high-quality solutions from the Goemans-Williamson algorithm, with results confirming that the optimal classical solution can be effectively further improved. The practical utility of the proposed algorithm is further validated through its application to haplotype phasing on genome sequencing data of the human ABCA1 gene, producing high-quality haplotypes that rival those obtained by the Goemans-Williamson algorithm with $10^6$ projections. These results establish the proposed algorithm as a scalable, NISQ-compatible framework for near-term quantum-enhanced large-scale combinatorial optimization.

quant-ph↗

arXiv2025

Quantum-Classical Hybrid Computation of Electron Transfer in a Cryptochrome Protein via VQE-PDFT and Multiscale Modeling

Accurate calculation of strongly correlated electronic systems requires proper treatment of both static and dynamic correlations, which remains challenging for conventional methods. To address this, we present VQE-PDFT,aquantum-classical hybrid framework that integrates variational quantum eigensolver with multiconfiguration pair-density functional theory (MC-PDFT). This framework strategically employs quantum circuits for multiconfigurational wavefunction representation while utilizing density functionals for correlation energy evaluation. The hybrid strategy maintains accurate treatment of static and dynamic correlations while reducing quantum resource requirements compared to highly expressive quantum algorithms. Benchmark validation, performed via noiseless quantum circuit simulator, on the Charge-Transfer dataset confirmed that VQE-PDFT achieved results comparable to conventional MC-PDFT. Building upon this, we developed shallow-depth hardware-efficient ansatz circuits and integrated them into a QM/MM multiscale architecture to enable applications in complex biological systems. This extended framework, when applied to electron transfer in the European robin cryptochrome protein ErCRY4 with noiseless simulations, yielded transfer rates that aligned well with experimental measurements. Finally, as a proof-of-concept hardware demonstration, we executed the reduced-density-matrix measurements for a single protein conformation on a 13-qubit superconducting device and showed the impact of noise through a comprehensive error analysis.

quant-ph↗

arXiv2025

Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods

The maximum parsimony phylogenetic tree reconstruction problem is NP-hard, presenting a computational bottleneck for classical computing and motivating the exploration of emerging paradigms like quantum computing. To this end, we design three optimization models compatible with both classical and quantum solvers. Our method directly searches the complete solution space of all possible tree topologies and ancestral states, thereby avoiding the potential biases associated with pre-constructing candidate internal nodes. Among these models, the branch-based model drastically reduces the number of variables and explicit constraints through a specific variable definition, providing a novel modeling approach effective not only for phylogenetic tree building but also for other tree problems. The correctness of this model is validated with a classical solver, which obtains solutions that are generally better than those from heuristics on the GAPDH gene dataset. Moreover, our quantum simulations successfully find the exact optimal solutions for small-scale instances with rapid convergence, highlighting the potential of quantum computing to offer a new avenue for solving these intractable problems in evolutionary biology.

quant-ph↗

arXiv2022

Ground States and Dynamical Properties of $S>1/2$ Quantum Heisenberg Model on the 1/5-Depleted Square Lattice

We study the $S>1/2$ antiferromagnetic Heisenberg model on the 1/5-depleted square lattice as a function of the ratio of the intra-plaquette coupling to the inter-plaquette coupling. Using stochastic series expansion quantum Monte Carlo simulations, we numerically identify three quantum phases, including the dimer phase, Néel phase and plaquette valence bond solid phase. We also obtain the accurate quantum critical points that belong to the O(3) universality class using the large-scale finite-size scaling. Most importantly, we study the dynamic spin structure factors of different phases, which can be measured by inelastic neutron scattering experiments. The low-energy excitations can be explained as triplons in the dimer phase and plaquette valence bond solid phase. While in the Néel phase, the more prominent magnon mode can be found as the spin magnitude increases. Furthermore, we find a broader continuum at smaller $S$, which may be the dynamical signature of nearly deconfined spinon excitations.

cond-mat.str-el↗

arXiv2021

Dynamical properties of the Haldane chain with bond disorder

By using Lanczos exact diagonalization and quantum Monte Carlo combined with stochastic analytic continuation, we study the dynamical properties of the $S=1$ antiferromagnetic Heisenberg chain with different strengths of bond disorder. In the weak disorder region, we find weakly coupled bonds which can induce additional low-energy excitation below the one-magnon mode. As the disorder increases, the average Haldane gap closes at $δ_Δ\sim 0.5$ with more and more low-energy excitations coming out. After the critical disorder strength $δ_c\sim 1$, the system reaches a random-singlet phase with prominent sharp peak at $ω=0$ and broad continuum at $ω>0$ of the dynamic spin structure factor. In addition, we analyze the distribution of random spin domains and numerically find three kinds of domains hosting effective spin-1/2 quanta or spin-1 sites in between. These "spins" can form the weakly coupled long-range singlets due to quantum fluctuation which contribute to the sharp peak at $ω=0$.

cond-mat.str-el↗

arXiv2020

Dynamical spin excitations of topological Haldane gapped phase in the $S=1$ Heisenberg antiferromagnetic chain with single-ion anisotropy

We study the dynamical spin excitations of the one-dimensional $S=1$ Heisenberg antiferromagnetic chain with single-ion anisotropy by using quantum Monte Carlo simulations and stochastic analytic continuation of imaginary-time correlation function. Using the transverse dynamic spin structure factor, we observe the quantum phase transition with a critical point between the topological Haldane gapped phase and the trivial phase. At the quantum critical point, we find a broad continuum characterized by the Tomonaga-Luttinger liquid similar to a $S=1/2$ Heisenberg antiferromagnetic chain. We further identify that the elementary excitations are fractionalized spinons.

cond-mat.str-el↗

arXiv2018

Tunable room-temperature ferromagnetism in the SiC monolayer

It is essential to explore two-dimensional (2D) material with magnetic ordering in new generation spintronic devices. Particularly, the seeking of room-temperature 2D ferromagnetic (FM) materials is a hot topic of current research. Here, we study magnetism of the Mn-doped and electron-doped SiC monolayer using first-principle calculations. For the Mn-doped SiC monolayer, we find that either electron or hole could mediate the ferromagnetism in the system and the Curie temperature ($T_C$) can be improved by appropriate carrier doping. The codoping strategy is also discussed on improving $T_C$. The transition between antiferromagnetic and FM phase can be found by strain engineering. The $T_C$ is improved above room temperature (RT) under the strain larger than $0.06$. Moreover, the Mn-doped SiC monolayer develops half-metal at the strain range of $0.05-0.1$. On the other hand, the direct electron doping can induce ferromagnetism due to the van Hove singularity in density of states of the conduction band edge of the SiC monolayer. The $T_C$ is found to be around RT. These fascinating controllable electronic and magnetic properties are desired for spintronic applications.

cond-mat.mes-hall↗