arXiv subjects

Zuoheng Zou

Publications and source records attributed to Zuoheng Zou.

9 recordsLinked to original sources

arXiv2026

Quantum-Enhanced Multi-Objective Optimization

Multi-objective combinatorial optimization requires identifying Pareto-optimal trade-off solutions among conflicting objectives, often making it more demanding than its single-objective counterpart. Although quantum multi-objective optimization methods have begun to emerge, most existing quantum optimization workflows are still built around single-objective or fixed-scalarization settings. Building on existing weighted-sum QAOA approaches to quantum multi-objective optimization, we propose QEMOO, a quantum-enhanced multi-objective optimization framework that combines Pareto-based selection and warm-started QAOA sampling in a multi-round protocol under the same total shot budget. We further introduce a PBI-inspired adaptive direction-update scheme to improve coverage in strongly conflicting benchmark regimes. Across three benchmark stages, QEMOO improves Pareto-front hypervolume over the single-pass weighted-sum QAOA baseline under matched shot budgets, suggesting a practical route toward shot-efficient quantum-assisted multi-objective optimization and its future applications.

quant-ph↗

arXiv2026

Variational simulation of higher-spin systems on qubit-based quantum simulators

Qubit-based quantum simulators naturally target two-level systems, whereas many quantum many-body problems are intrinsically $d$ level. Encodings from qudits to qubits then enlarge the Hilbert space and can introduce unphysical states that interfere with variational optimization. We formulate a variational framework for encoded $d$-level models that suppresses these illegitimate states with penalty terms and benchmark it for spin-1 and spin-3/2 bilinear-biquadratic Heisenberg chains. We compare binary encoding, which minimizes the qubit overhead, with symmetry encoding, which preserves the relevant spin symmetries and enables symmetry-conserving ansätze. Although binary encoding is more qubit efficient, its hardware-efficient ansatz is harder to train and less effective at exploiting conserved quantities. In contrast, symmetry encoding requires more qubits but reaches substantially higher fidelities, converges faster, and exhibits better trainability than the binary hardware-efficient ansatz. These results identify symmetry-preserving encodings as a practical route to simulating higher-spin models on existing qubit platforms.

quant-ph↗

arXiv2026

Pilot-Wave Simulator: Exact Classical Sampling from Ideal and Noisy Quantum Circuits up to Hundreds of Qubits

Quantum circuit simulators running on classical computers offer a vital platform for designing, testing, and optimizing quantum algorithms, driving innovation despite limited access to real quantum hardware. However, their scalability is inherently constrained by exponential memory and computational overhead, which restricts accurate simulation of large-scale quantum circuits and often results in approximate output distributions. Here, we propose an exact sampling algorithm that integrates tensor network contraction techniques with a Markov process, wherein a classical state evolves according to the local structure of the quantum circuit. As a demonstration, we target the challenge of generating samples from ideal and noisy QAOA circuits with up to 476 qubits, incorporating both depolarizing and amplitude damping noise models. These results enable further validation of several assumptions and conjectures at a scale previously out of reach, significantly expanding the scope of classical simulation in quantum algorithm research.

quant-ph↗

arXiv2025

Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits

A pivotal task for quantum computing is to speed up solving problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad applicability and natural fitness to Ising Hamiltonians. Here we propose a quantum sampling strategy, based on which we design an algorithm for accelerating solving the ground states of Ising model, a class of NP-hard problems in combinatorial optimization. The algorithm employs a hybrid quantum-classical workflow, with a shallow-circuit quantum sampling subroutine dedicated to navigating the energy landscape. Using up to 104 superconducting qubits, we demonstrate that this algorithm outputs favorable solutions against even a highly-optimized classical simulated annealing (SA) algorithm. Furthermore, we illustrate the path toward quantum speedup based on the time-to-solution metric against SA running on a single-core CPU with just 100 qubits. Our results indicate a promising alternative to classical heuristics for combinatorial optimization, a paradigm where quantum advantage might become possible on near-term superconducting quantum processors with thousands of qubits and without the assistance of error correction.

quant-ph↗

arXiv2025

Cost of Locally Approximating High-Dimensional Ground States of Contextual Quantum Models

Contextuality, one of the strongest forms of quantum correlations, delineates the quantum world and the classical one. It has been shown recently that some quantum models, in the form of infinite one-dimensional translation-invariant Hamiltonians with nearest- and next-to-nearest-neighbor interactions, have the lowest ground state energy density allowed in quantum physics. However, these models all have local Hilbert space dimension larger than two, making the study of their ground state behavior difficult on current qubit-based variational quantum simulation platforms. In this work, we focus on the cost of simulating the local approximations of ground states of these models using qubit-based parameterized quantum circuits. The local approximations, which are 3-site reduced density matrices with local Hilbert space dimension three, are purified then encoded into permutation-symmetric qubits. We develop a universal set of permutation-symmetry preserving qubit-based gates, using them as an ansatz to simulate parameterized quantum circuits designed for qutrits. These techniques allow us to assess the accuracy of simulating the purified local ground states with respect to a fixed amount of classical and quantum resources. We found that given the same quantum circuit and the number of iterations, more contextual ground states with lower energy density are easier to simulate.

quant-ph↗

arXiv2024

MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance.

quant-ph↗

arXiv2024

Efficient molecular conformation generation with quantum-inspired algorithm

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.

quant-ph↗

arXiv2023

First-principle calculation of $η_c\rightarrow 2γ$ decay width from lattice QCD

We perform a lattice QCD calculation of the $η_c\to2γ$ decay width using a model-independent method that requires no momentum extrapolation of the off-shell form factors. This method also provides a straightforward and simple way to examine the finite-volume effects. The calculation is accomplished using $N_f=2$ twisted mass fermion ensembles. The statistically significant excited-state effects are observed and eliminated using a multi-state fit.The impact of fine-tuning the charm quark mass is also examined and confirmed to be well-controlled. Finally, using three lattice spacings for the continuum extrapolation, we obtain the decay width $Γ_{η_cγγ}=6.67(16)_{\mathrm{stat}}(6)_{\mathrm{syst}}$ keV, which differs significantly from the Particle Data Group's reported value of $Γ_{η_cγγ}=5.4(4)$ keV (2.9~$σ$ tension). We provide insight into the comparison between our findings, previous theoretical predictions, and experimental measurements.

hep-lat↗

arXiv2022

Lattice calculation of $χ_{c0} \rightarrow 2γ$ decay width

We perform a lattice QCD calculation of the $χ_{c0} \rightarrow 2γ$ decay width using a model-independent method which does not require a momentum extrapolation of the corresponding off-shell form factors. The simulation is performed on ensembles of $N_f=2$ twisted mass lattice QCD gauge configurations with three different lattice spacings. After a continuum extrapolation, the decay width is obtained to be $Γ_{γγ}(χ_{c0})=3.65(83)_{\mathrm{stat}}(21)_{\mathrm{lat.syst}}(66)_{\mathrm{syst}}\, \textrm{keV}$. Albeit this large statistical error, our result is compatible with the experimental results within 1.3$σ$. Potential improvements of the lattice calculation in the future are also discussed.

hep-lat↗