arXiv subjects

Min-Quan He

Publications and source records attributed to Min-Quan He.

7 recordsLinked to original sources

arXiv2026

Revealing Physical Redundancy in the Two-dimensional Fermi-Hubbard Model via Transferable Observable Reconstruction

The Fermi-Hubbard model provides a paradigmatic setting for studying strongly correlated quantum matter, where different observables are commonly used to probe charge, interaction, and spin correlations. In this work, we investigate whether these observables contain mutually transferable physical information beyond their apparent distinction. We quantify such physical redundancy through transferability tests among three representative observables of the two-dimensional Fermi-Hubbard model: total density (N), double occupancy (D), and spin-spin correlation (S). Using a neural-network reconstruction framework, we find that the phase diagram of one observable can be reconstructed from another with accuracy close to self-reconstruction benchmarks, especially in trivial phase regimes. This transferability relies on correct physical labeling, persists across finite-temperature regimes, and remains robust under noisy inputs. Our results suggest that separate observables can carry a substantial fraction of one another's physical information, providing numerical evidence for observable-level redundancy in the two-dimensional Fermi-Hubbard system.

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arXiv2025

AI-enhanced Quantum Simulation of Schwinger Model

The Schwinger Model from Quantum Electrodynamics (QED) has long served as a valuable simplified model for exploring key physical phenomena in Quantum Chromodynamics (QCD)-a field rich with fundamental insights but is substantially more complex. While the phase diagram of the Schwinger Model bears extraordinary significance and remains challenging to investigate, recent progress on the model mainly focuses on detailed case studies. Here, we propose a model that we refer as the Neural Network Facilitated Implicit Quantum Simulation (NN-IQS) model as a solution. After training on limited discrete data points on the Schwinger Model phase diagram, the NN-IQS model allows quick generation of extra sample points over a continuous domain. The model can even generalize beyond its training range, maintaining robust performance in previously unexplored parameter space and system sizes.

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arXiv2025

Public-Key Quantum Authentication and Digital Signature Schemes Based on the QMA-Complete Problem

We propose a quantum authentication and digital signature protocol whose security is founded on the Quantum Merlin Arthur~(QMA)-completeness of the consistency of local density matrices. The protocol functions as a true public-key cryptography system, where the public key is a set of local density matrices generated from the private key, a global quantum state. This construction uniquely eliminates the need for trusted third parties, pre-shared secrets, or authenticated classical channels for public key distribution, making a significant departure from symmetric protocols like quantum key distribution. We provide a rigorous security analysis, proving the scheme's unforgeability against adaptive chosen-message attacks by quantum adversaries. The proof proceeds by a formal reduction, demonstrating that a successful forgery would imply an efficient quantum algorithm for the QMA-complete Consistency of Quantum Marginal Problem~(QMP). We further analyze the efficiency of verification using partial quantum state tomography, establishing the protocol's theoretical robustness and outlining a path towards practical implementation

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arXiv2024

Quantum coupon collector with mixed-state encoding

The coupon collector is a prototypical model for evaluating the number of samples for identifying a set. By superposing all elements in the set as a pure quantum state, a quantum version of the coupon collector aims to learn the state, which is shown to reduce the sample complexity. Here we propose a quantum coupon collector by encoding the set into a mixed state, where the information of missing elements are labelled with Pauli strings. Remarkably, the encoded mixed state has no quantum entangled state and is easy to prepare. With such mixed-state encoding, it can be efficient to learn the set by performing Bell measurements on two copies and then extracting the missing element by solving a series of equations obtained from the measurements. Our protocol further reduces the sample complexity from $O(n)$ in the case of pure-state encoding to $O(\log n)$ when the missing element is one, where $n$ is the number of elements in the set. The mixed-state encoding scheme provides a new avenue for quantum learning and enlarges the realm for exploring quantum advantages.

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arXiv2024

Variational Quantum Eigensolvers with Quantum Gaussian Filters for solving ground-state problems in quantum many-body systems

We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems, particularly suited for Noisy Intermediate-Scale Quantum (NISQ) devices. Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF), utilizing an iterative methodology that discretizes the application of the QGF operator into small, optimized steps through VQE. Demonstrated on the Transverse Field Ising models, our method shows improved convergence speed and accuracy, particularly under noisy conditions, compared to conventional VQE methods. This advancement highlights the potential of our algorithm in effectively addressing complex quantum simulations, marking a significant stride in quantum computing applications within the NISQ era.

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arXiv2022

Quantum Gaussian filter for exploring ground-state properties

Filter methods realize a projection from a superposed quantum state onto a target state, which can be efficient if two states have sufficient overlap. Here we propose a quantum Gaussian filter (QGF) with the filter operator being a Gaussian function of the system Hamiltonian. A hybrid quantum-classical algorithm feasible on near-term quantum computers is developed, which implements the quantum Gaussian filter as a linear combination of Hamiltonian evolution at various times. Remarkably, the linear combination coefficients are determined classically and can be optimized in the postprocessing procedure. Compared to the existing filter algorithms whose coefficients are given in advance, our method is more flexible in practice under given quantum resources with the help of postprocessing on classical computers. We demonstrate the quantum Gaussian filter algorithm for the quantum Ising model with numeral simulations under noises. We also propose an alternative full quantum approach that implements a QGF with an ancillary continuous-variable mode.

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arXiv2022

Inverse iteration quantum eigensolvers assisted with a continuous variable

The capacity for solving eigenstates with a quantum computer is key for ultimately simulating physical systems. Here we propose inverse iteration quantum eigensolvers, which exploit the power of quantum computing for the classical inverse power iteration method. A key ingredient is constructing an inverse Hamiltonian as a linear combination of coherent Hamiltonian evolution. We first consider a continuous-variable quantum mode (qumode) for realizing such a linear combination as an integral, with weights being encoded into a qumode resource state. We demonstrate the quantum algorithm with numerical simulations under finite squeezing for various physical systems, including molecules and quantum many-body models. We also discuss a hybrid quantum-classical algorithm that directly sums up Hamiltonian evolution with different durations for comparison. It is revealed that continuous-variable resources are valuable for reducing the coherent evolution time of Hamiltonians in quantum algorithms.

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