arXiv subjects

Xiongzhi Zeng

Publications and source records attributed to Xiongzhi Zeng.

14 recordsLinked to original sources

arXiv2026

Perturbative Variational Quantum Eigensolver via Reduced Density Matrices

Current noisy intermediate-scale quantum (NISQ) devices remain limited in their ability to perform accurate quantum chemistry simulations due to restricted numbers of high-fidelity qubits and short coherence times. To overcome these challenges, we introduce a reduced density matrix (RDM)-based perturbative variational quantum eigensolver (VQE) framework that augments active-space VQE with perturbation theory to recover electron correlation beyond the active space without increasing the qubit count or variational circuit depth. We formulate a fully coupled approach (VQE-PTs) and a diagonal approximation (VQE-PT). The former retains couplings among orthonormalized perturbers, whereas the latter neglects these couplings to simplify the classical post-processing. Numerical simulations of HF, N$_2$, and F$_2$ show that VQE-PTs provides a robust formulation across different molecular systems, while VQE-PT offers an efficient approximation. We further experimentally implement VQE-PT on the Quafu superconducting quantum processor for F$_2$, achieving a mean absolute error of 1.2 millihartree along the potential energy surface after error mitigation. These results demonstrate perturbative VQE as a practical framework for incorporating dynamic correlation in quantum chemistry simulations.

quant-ph↗

arXiv2026

Embedded quantum computing for many-body surface reaction

Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28 qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded quantum computing as a practical route to correlated surface-reaction energetics.

quant-ph↗

arXiv2026

False Positives Raised by Quantum Readout Error Mitigation

Quantum readout error mitigation is essential for noisy intermediate-scale quantum devices to achieve reliable data. The conventional approaches, conflating initialization errors with measurement errors, not only suppress the influence of measurement errors, but also strengthen that of initialization errors, which is a systematic bias grows exponentially with the qubit number. Here, we have proved that this effect causes severe fidelity overestimation for all stabilizer states and might lead to false positives in large-scale entangled state characterization. Similarly, the results from algorithms like the variational quantum eigensolver and time evolution also deviate negatively, and cover up other errors in the quantum circuit. These findings highlight the critical need for rigorous benchmarking and careful management of initialization errors. Consequently, we establish an upper bound for the tolerable initialization error rate to ensure effective error mitigation at a given system scale.

quant-ph↗

arXiv2026

Transformer refined quantum sampling for strongly correlated electronic structure

Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices.

quant-ph↗

arXiv2025

Quantum-centric machine learning for molecular dynamics

Accurate and efficient prediction of electronic wavefunctions is central to ab initio molecular dynamics (AIMD) and electronic structure theory. However, conventional ab initio methods require self-consistent optimization of electronic states at every nuclear configuration, leading to prohibitive computational costs, especially for large or strongly correlated systems. Here, we introduce a quantum-centric machine learning (QCML) model-a hybrid quantum-classical framework that integrates parameterized quantum circuits (PQCs) with Transformer-based machine learning to directly predict molecular wavefunctions and quantum observables. By pretraining the Transformer on a diverse dataset of molecules and ansatz types and subsequently fine-tuning it for specific systems, QCML learns transferable mappings between molecular descriptors and PQC parameters, eliminating the need for iterative variational optimization. The pretrained model achieves chemical accuracy in potential energy surfaces, atomic forces, and dipole moments across multiple molecules and ansatzes, and enables efficient AIMD simulations with infrared spectra prediction. This work establishes a scalable and transferable quantum-centric machine learning paradigm, bridging variational quantum algorithms and modern deep learning for next-generation molecular simulation and quantum chemistry applications.

physics.chem-ph↗

arXiv2025

First Principles Reactive Flux Theory for Surface Reactions: Multiple Channels and Recrossing Dynamics

Heterogenous reactions typically consist of multiple elementary steps and their rate coefficients are of fundamental importance in elucidating the mechanisms and micro-kinetics of these processes. Transition-state theory (TST) for calculating surface reaction rate coefficients often relies solely on the harmonic approximation of adsorbent vibrations and neglects recrossing dynamics. Here, we combine, for the first time, an efficient metadynamics enhanced sampling method with a more general reactive flux approach to calculate rate coefficients of surface reactions of any order and/or with multiple reaction coordinates, overcoming these limitations of TST. We apply this approach to a textbook surface reaction, CO oxidation on Pt(111), for which rate constants have been precisely measured, using a full-dimensional neural network potential energy surface constructed from first-principles data. An accurate multi-dimensional free-energy surface is obtained by incorporating three collective variables, yielding rate coefficients for both CO oxidation and the competing CO desorption that are in good agreement with experimental data. Interestingly, our results reveal significant dynamic recrossing in both channels, which however arises from distinct physical mechanisms. This approach represents an accurate and general framework for calculating rate coefficients of elementary surface processes from first-principles, which is vital for developing predictive kinetic models for heterogenous catalysis.

physics.chem-ph↗

arXiv2024

Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schrödinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer.

quant-ph↗

arXiv2024

Accurate Chemical Reaction Modeling on Noisy Intermediate-Scale Quantum Computers Using a Noise-Resilient Wavefunction Ansatz

Quantum computing is of great potential for chemical system simulations. In this study, we propose an efficient protocol of quantum computer based simulation of chemical systems which enables accurate chemical reaction modeling on noisy intermediate-scale quantum (NISQ) devices. In this protocol, we combine an correlation energy-based active orbital selection, an effective Hamiltonian from the driven similarity renormalization group (DSRG) method, and a noise-resilient wavefunction ansatz. Such a combination gives a quantum resource-efficient way to accurately simulate chemical systems. The power of this protocol is demonstrated by numerical results for systems with up to tens of atoms. Modeling of a Diels-Alder (DA) reaction is also performed on a cloud-based superconducting quantum computer. These results represent an important step forward in realizing quantum utility in the NISQ era.

quant-ph↗

arXiv2023

Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems

The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wavefunction ansatz. Compared to physically motivated ansatzes, hardware heuristic ansatzes usually lead to a shallower circuit, but it may still be too deep for an NISQ device. Inspired by the quantum neural network, we propose a new hardware heuristic ansatz where the circuit depth can be significantly reduced by introducing ancilla qubits, which makes a practical simulation of a chemical reaction with more than 20 atoms feasible on a currently available quantum computer. More importantly, the expressibility of this new ansatz can be improved by increasing either the depth or the width of the circuit, which makes it adaptable to different hardware environments. These results open a new avenue to develop practical applications of quantum computation in the NISQ era.

quant-ph↗

arXiv2022

Q$^2$Chemistry: A quantum computation platform for quantum chemistry

Quantum computer provides new opportunities for quantum chemistry. In this article, we present a versatile, extensible, and efficient software package, named Q$^2$Chemistry, for developing quantum algorithms and quantum inspired classical algorithms in the field of quantum chemistry. In Q$^2$Chemistry, wave function and Hamiltonian can be conveniently mapped into the qubit space, then quantum circuits can be generated according to a specific quantum algorithm already implemented in the package or newly developed by the users. The generated circuits can be dispatched to either a physical quantum computer, if available, or to the internal virtual quantum computer realized by simulating quantum circuit on classical supercomputers. As demonstrated by our benchmark simulations with up to 72 qubit, Q$^2$Chemistry achieves excellent performance in simulating medium scale quantum circuits. Application of Q$^2$Chemistry to simulate molecules and periodic systems are given with performance analysis.

quant-ph↗

arXiv2022

Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning

The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an appealing application of quantum computing, we show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques as a promising scheme for exploring accurate PESs. Different from using a ML model to represent the potential energy, we encode the molecular geometry information into a deep neural network (DNN) for representing parameters of the variational quantum eigensolver (VQE), leaving the PES to the wave function ansatz. Once the DNN model is trained, the variational optimization procedure that hinders the application of the VQE to complex systems is avoided and thus the evaluation of PESs is significantly accelerated. Numerical results demonstrate that a simple DNN model is able to reproduce accurate PESs for small molecules.

physics.chem-ph↗

arXiv2022

Understanding High-Temperature Chemical Reactions on Metal Surfaces

Chemical reactions on metal surfaces are important in various processes such as heterogeneous catalysis and nanostructure growth. At moderate or lower temperatures, these reactions generally follow the minimum energy path and temperature effects can be reasonably described by a harmonic oscillator model. At a high temperature approaching the melting point of the substrate, general behaviors of surface reactions remain elusive. In this study, by taking hydrocarbon species adsorbed on Cu(111) as a model system and performing extensive molecular dynamics simulations powered by machine learning potentials, we identify several important high-temperature effects, including local chemical environment, surface atom mobility, and substrate thermal expansion. They affect different aspects of a high-temperature surface reaction in different ways. These results deepen our understanding of high-temperature reactions.

physics.chem-ph↗

arXiv2020

Can High-Temperature Reactions Be Described by a Minimum Energy Path Model? Steric Hindrance Matters

High-temperature reactions widely exist in nature. However, they are difficult to be characterized either experimentally or computationally. The routinely used minimum energy path (MEP) model in computational modeling of chemical reactions is not justified to describe high-temperature reactions since high-energy structures are actively involved there. In this study, using CH4 decomposition on the Cu(111) surface as an example, we systematically compare MEP results with those obtained by explicitly sampling all relevant structures via ab initio molecular dynamics (AIMD) simulations at different temperatures. Interestingly, we find that, for reactions protected by a strong steric hindrance effect, the MEP is still effectively followed even at a temperature close to the Cu melting point. In contrast, without such a protection, the flexibility of surface Cu atoms can lead to a significant free energy barrier reduction at a high temperature. Accordingly, some conclusions about graphene growth mechanisms based on MEP calculations should be revisited. Physical insights provided by this study can deepen our understanding on high-temperature surface reactions.

physics.chem-ph↗

arXiv2018

Electron-phonon interaction in Ca2N monolayer: intrinsic mobility of electrene

Electron-phonon(e-ph) interaction in Ca2N monolayer, the first electrene material with two-dimensional(2D) electron gas floating in free space, is expected to be very weak and such a character can be used to design weak-scattering transport channels. Therefore, it is highly desirable to quantitatively evaluate the carrier mobility of electrene. In this study, e-ph interaction in Ca2N monolayer is investigated using a precise Wannier interpolation-based first principles technique. The calculated e-ph coupling matrix elements of Ca2N monolayer are indeed small compared to other 2D materials such as graphene, which leads to an intrinsic mobility of 189 cm2V-1s-1, much higher than those of conventional metals. Other factors affecting mobility are discussed in a comparison with graphene. It is predicted that, based on a momentum mismatch mechanism, mobility of Ca2N monolayer can be increased further to above 3000 cm2V-1s-1 via hole doping. Our results confirm that Ca2N electrene is a promising electronic material.

physics.comp-ph↗