arXiv subjects

Dingshun Lv

Publications and source records attributed to Dingshun Lv.

At least 19 recordsLinked to original sources

arXiv2026

Screening phonon-mediated superconductors from static orbital Hamiltonians

The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transparent framework that identifies strong-EPC materials directly from static orbital-based Hamiltonians without explicit phonon perturbation calculations. Verification using density functional perturbation theory (DFPT) for representative superconductors shows that the framework captures semi-quantitatively the EPC scale at substantially lower computational cost. Applied to more than 36,000 compounds in the MattKeyBond database, it identifies 34 dynamically stable superconducting candidates with calculated $T_c > 10$ K after DFPT verification. These candidates reveal two distinct routes to relatively high-$T_c$ superconductivity: a metallized covalent $σ$-bond route that is more favorable for achieving high-$T_c$ superconductors, and a Fermi-level density-of-states accumulation route that can enhance $T_c$ but usually to a more limited extent.

cond-mat.supr-con↗

arXiv2026

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors

Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows.

cond-mat.mtrl-sci↗

arXiv2026

A Unified Generative Framework for Scalable Chemical Reaction Network Exploration

Chemical reaction networks (CRNs) are crucial for understanding reaction mechanisms and guiding chemical synthesis, yet the computational exploration remains limited by the combinatorial growth of chemical space, the reliability of reaction path screening, and the cost of evaluating thermodynamic and kinetic properties. Here, we present ByteCRN, an end-to-end framework for computational CRN exploration that combines chemically informed reaction enumeration with generative transition state modeling. A key component of our framework is a generative rectified flow architecture for both transition state generation and reaction validation, where it maps reactant-product pairs to candidate transition state structures and verifies connectivity by mapping back to reactants and products. This unified generative strategy replaces the most expensive steps of conventional computational workflows, namely iterative transition state search and intrinsic reaction coordinate validation, within a complete CRN construction pipeline. ByteCRN delivers a 10--100-fold acceleration over traditional workflows while maintaining high predictive fidelity for individual reactions. At the network scale, it effectively prunes $\sim$70-90% of the enumerated reactions, streamlining the exploration of complex reaction space. Its utility is illustrated through the discovery of novel pathways involving cyanoacetaldehyde and the successful modeling of the challenging $γ$-ketohydroperoxide network, demonstrating a practical, scalable approach to autonomous chemical exploration.

physics.chem-ph↗

arXiv2026

Real-Time Quantum Error Correction System Stack: Architecture, Algorithms, and Engineering Practice

Quantum error correction (QEC) is transitioning from physical feasibility demonstrations to systems engineering challenges. Google has achieved below-threshold performance on distance-5/7 surface codes, while Riverlane and Rigetti have demonstrated hardware-integrated low-latency feedback loops. These milestones indicate that the core challenge of real-time decoding has shifted from algorithmic capability to system-level engineering. However, a substantial engineering gap remains between laboratory demonstrations and scalable fault-tolerant quantum computing (FTQC). This white paper addresses three questions: (1) Where are the real bottlenecks in real-time QEC: beyond average decoder speed, the constraints lie in QEC round time, tail latency, and end-to-end data path coordination; (2) How mature are mainstream decoder algorithms: we benchmark the major decoders for both surface codes and quantum low-density parity-check (qLDPC) codes, evaluating their real-time readiness; (3) What system stack do we propose: a six-layer reference architecture from syndrome acquisition to logical operations, with interface definitions and latency budget models. Our results quantify the gap between current decoder performance and real-time requirements, and identify the architectural choices needed to close it.

quant-ph↗

arXiv2026

Pareto Frontier of Neural Quantum States: Scalable, Affordable, and Accurate Convolutional Backflow for Strongly Correlated Lattice Fermions

Neural Quantum States (NQS) are now among the most accurate methods for studying strongly correlated many-fermion systems, outperforming existing many-body approaches for large systems. However, NQS calculations remain extremely resource-intensive. Here, we introduce a new Pareto frontier of efficiency and accuracy for NQS in simulating strongly correlated lattice fermions, defined by two complementary backflow-related architectures: the Sparse Convolutional Ansatz for Lattice Electrons (SCALE) (state-of-the-art efficiency) and the Accurate Convolutional ansatz for lattice Electrons (ACE) (state-of-the-art accuracy), benchmarked on the iconic Hubbard and $t-J$ models for large lattices. SCALE uses a tailored convolutional design enabling efficient local updates via low-rank determinant updates, reducing computational scaling from $O(N^4)$ to $O(N^3)$ in backflow methods and yielding a >40$\times$ practical speed-up in tests while maintaining high variational accuracy. As an application, we study the previously inaccessible 1/8-doped pure Hubbard model up to $32 \times 32$, finding no significant energy difference between horizontal and vertical filled stripe states - contrasting with half-filled stripe states when next-nearest-neighbor hoppings are included. ACE employs a deep convolutional stack to maximize expressive power, achieving unprecedented accuracy on large systems. Extensive benchmarks on Hubbard and $t-J$ models show SCALE delivers variational energies competitive with leading methods at a fraction of the cost, while ACE sets a new accuracy benchmark, surpassing recent results with only 1/6 the runtime for $16 \times 4$ systems. These new NQS approaches provide scalable, affordable, and accurate tools for exploring strongly correlated fermionic physics, such as the microscopic mechanism of unconventional superconductivity.

cond-mat.str-el↗

arXiv2026

Quantum Many-Body Simulations of Catalytic Metal Surfaces

Quantum simulations of metal surfaces are critical for catalytic innovation. Yet existing methods face a cost-accuracy dilemma: density functional theory is efficient but system-dependent in accuracy, while wavefunction-based theories are accurate but prohibitively costly. Here we introduce FEMION (Fragment Embedding for Metals and Insulators with Onsite and Nonlocal correlation), a systematically improvable quantum embedding framework that resolves this challenge by capturing partially filled electronic states in metals. FEMION combines auxiliary-field quantum Monte Carlo for local catalytic sites with a global random phase approximation treatment of nonlocal screening, yielding a scalable approach across diverse catalytic systems. Employing FEMION, we address two longstanding challenges: determining the preferred CO adsorption site and quantifying the H2 desorption barrier on Cu(111). Furthermore, our calculations demonstrate that the recently discovered 10-electron-count rule can also be extended to the single-atom catalysis processes on 3d metal surfaces, resolving the controversies arising from density functional theory calculations. We thus open a predictive, first-principles route to modeling complex catalytic systems.

physics.chem-ph↗

arXiv2026

Hessian-informed machine learning interatomic potential towards bridging theory and experiments

Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling accurate analysis of associated thermodynamic and kinetic phenomena. To make Hessian supervision practically viable, we develop a highly efficient training protocol, termed Hessian INformed Training (HINT), achieving two to four orders of magnitude reduction for the requirement of expensive Hessian labels. HINT integrates critical techniques, including Hessian pre-training, configuration sampling, curriculum learning and stochastic projection Hessian loss. Enabled by HINT, Hi-MLIP significantly improves transition-state search and brings Gibbs free-energy predictions close to chemical accuracy especially in data-scarce regimes. Our framework also enables accurate treatment of strongly anharmonic hydrides, reproducing phonon renormalization and superconducting critical temperatures in close agreement with experiment while bypassing the computational bottleneck of anharmonic calculations. These results establish a practical route to enhancing curvature awareness of machine learning interatomic potentials, bridging simulation and experimental observables across a wide range of systems.

cs.LG↗

arXiv2026

Disentangling Tensor Network States with Deep Neural Network

We introduce Neural Tensor Network States ($ν$TNS), a variational many-body wave-function ansatz that integrates deep neural networks with tensor-network architectures. In the $ν$TNS framework, a neural network serves as a disentangler of the wave-function, transforming the physical degrees of freedom into renormalized variables with much less entanglement. The renormalized state is then efficiently encoded by a back-flow tensor network. This construction yields a compact yet highly expressive representation of strongly correlated quantum states. Using convolutional neural networks combined with matrix product states as a concrete implementation, we obtain state-of-the-art variational energies for the spin-$1/2$ $J_1$-$J_2$ Heisenberg model on the square lattice at the highly frustrated point $J_2/J_1=0.5$, for systems up to $20\times 20$ with periodic boundary conditions. Finite-size scaling of spin, dimer, and plaquette correlations exhibits power-law decay without magnetic or valence-bond long-range order, consistent with a gapless quantum spin-liquid ground state at that point.This $ν$TNS framework is flexible and naturally extensible to other neural and tensor-network structures, offering a general platform for investigating strongly correlated quantum many-body systems.

cond-mat.str-el↗

arXiv2025

Quantum computing quantum Monte Carlo algorithm

Quantum computing and quantum Monte Carlo (QMC) are respectively the state-of-the-art quantum and classical computing methods for understanding many-body quantum systems. Here, we propose a hybrid quantum-classical algorithm that integrates these two methods, inheriting their distinct features in efficient representation and manipulation of quantum states and overcoming their limitations. We first introduce non-stoquasticity indicators (NSIs) and their upper bounds, which measure the sign problem, the most notable limitation of QMC. We show that our algorithm could greatly mitigate the sign problem, which decreases NSIs with the assistance of quantum computing. Meanwhile, the use of quantum Monte Carlo also increases the expressivity of shallow quantum circuits, allowing more accurate computation that is conventionally achievable only with much deeper circuits. We numerically test and verify the method for the N$_2$ molecule (12 qubits) and the Hubbard model (16 qubits). Our work paves the way to solving practical problems with intermediate-scale and early-fault tolerant quantum computers, with potential applications in chemistry, condensed matter physics, materials, high energy physics, etc.

quant-ph↗

arXiv2025

Digital adiabatic evolution is universally accurate

Adiabatic evolution is a central paradigm in quantum physics. Digital simulations of adiabatic processes are generally viewed as costly, since algorithmic errors typically accumulate over the long evolution time, requiring exceptionally deep circuits to maintain accuracy. This work demonstrates that digital adiabatic evolution is intrinsically accurate and robust to simulation errors. We analyze two Hamiltonian simulation methods -- Trotterization and generalized quantum signal processing -- and prove that the simulation error does not increase with time. Numerical simulations of molecular systems and linear equations confirm the theory, revealing that digital adiabatic evolution is substantially more efficient than previously assumed. Remarkably, our estimation for the first-order Trotterization error can be 10^6 times tighter than previous analyses for the transverse field Ising model even with less than 6 qubits. The findings establish fundamental robustness of digital adiabatic evolution and provide a basis for accurate, efficient implementations on fault-tolerant -- and potentially near-term -- quantum platforms.

quant-ph↗

arXiv2025

Solving the Hubbard model with Neural Quantum States

The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edge transformer-based architectures and developing highly efficient optimization algorithms, we achieve the state-of-the-art results for the doped two-dimensional (2D) Hubbard model, arguably the minimum model for high-Tc superconductivity. Interestingly, we find different attention heads in the NQS ansatz can directly encode correlations at different scales, making it capable of capturing long-range correlations and entanglements in strongly correlated systems. With these advances, we establish the half-filled stripe in the ground state of 2D Hubbard model with the next nearest neighboring hoppings, consistent with experimental observations in cuprates. Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.

cond-mat.str-el↗

arXiv2025

Superconducting phase interference effect in momentum space

Detecting the phases of the superconducting order parameter is pivotal for unraveling the pairing symmetry of superconducting electrons. Conventional methods for probing these phases have focused on macroscopic interference effects, such as the Josephson effect. However, at the microscopic level, phase interference effects within momentum space have remained elusive due to the inherent difficulty of extracting phase information from individual momentum points. By introducing the hybridization effect between a primary band and its replica bands arising from density wave orders or other interactions, we uncover a novel superconducting phase interference effect at the intersection points on the Fermi surfaces of these bands. This effect elucidates the remarkable anomalies recently observed in the single-particle spectral function through angle-resolved photoemission spectroscopy (ARPES) in $Bi_2Sr_2CaCu_2O_{8+δ}$ (Bi2212) superconductors. It can also emerge in twisted junctions of superconductors with coherent tunneling, offering an alternative framework for probing the relative superconducting phase through twisted superstructures.

cond-mat.supr-con↗

arXiv2025

ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems

Applying quantum chemistry algorithms to large-scale systems requires substantial computational resources scaled with the system size and the desired accuracy. To address this, ByteQC, a fully-functional and efficient package for large-scale quantum chemistry simulations, has been open-sourced at https://github.com/bytedance/byteqc, leveraging recent advances in computational power and many-body algorithms. Regarding computational power, several standard algorithms are efficiently implemented on modern GPUs, ranging from mean-field calculations (Hartree-Fock and density functional theory) to post-Hartree-Fock methods such as Møller-Plesset perturbation theory, random phase approximation, coupled cluster methods, and quantum Monte Carlo methods. For the algorithmic approach, we also employ a quantum embedding method, which significantly expands the tractable system size while preserving high accuracy at the gold-standard level. All these features have been systematically benchmarked. For standalone algorithms, the benchmark results demonstrate up to a 60$\times$ speedup when compared to 100-core CPUs. Additionally, the tractable system sizes have been significantly expanded: 1,610 orbitals for coupled cluster with single and double excitations (1,380 orbitals with perturbative triple excitations), 11,040 orbitals for Møller-Plesset perturbation theory of second order, 37,120 orbitals for mean-field calculations under open boundary conditions, and over 100,000 orbitals for periodic boundary conditions. For the advanced quantum embedding feature, two representative examples are demonstrated: the water cluster problem (2,752 orbitals) and a water monomer adsorbed on a boron nitride surface (3,929 orbitals), achieving the gold-standard accuracy.

physics.chem-ph↗

arXiv2025

Fault-tolerant quantum algorithms for quantum molecular systems: A survey

Solving quantum molecular systems presents a significant challenge for classical computation. The advent of early fault-tolerant quantum computing (EFTQC) devices offers a promising avenue to address these challenges, leveraging advanced quantum algorithms with reduced hardware requirements. This review surveys the latest developments in EFTQC and fully fault-tolerant quantum computing (FFTQC) algorithms for quantum molecular systems, covering encoding schemes, advanced Hamiltonian simulation techniques, and ground-state energy estimation methods. We highlight recent progress in overcoming practical barriers, such as reducing circuit depth and minimizing the use of ancillary qubits. Special attention is given to the potential quantum advantages achievable through these algorithms, as well as the limitations imposed by dequantization and classical simulation techniques. The review concludes with a discussion of future directions, emphasizing the need for optimized algorithms and experimental validation to bridge the gap between theoretical developments and practical implementation in EFTQC and FFTQC for quantum molecular systems.

quant-ph↗

arXiv2025

Advancing Surface Chemistry with Large-Scale Ab-Initio Quantum Many-Body Simulations

Predictive simulation of surface chemistry is of paramount importance for progress in fields from catalysis to electrochemistry and clean energy generation. Ab-initio quantum many-body methods should be offering deep insights into these systems at the electronic level, but are limited in their efficacy by their steep computational cost. In this work, we build upon state-of-the-art correlated wavefunctions to reliably converge to the `gold standard' accuracy in quantum chemistry for application to extended surface chemistry. Efficiently harnessing graphics processing unit acceleration along with systematically improvable multiscale resolution techniques, we achieve linear computational scaling up to 392 atoms in size. These large-scale simulations demonstrate the importance of converging to these extended system sizes, achieving a validating handshake between simulations with different boundary conditions for the interaction of water on a graphene surface. We provide a new benchmark for this water-graphene interaction that clarifies the preference for water orientations at the graphene interface. This is extended to the adsorption of carbonaceous molecules on chemically complex surfaces, including metal oxides and metal-organic frameworks, where we consistently achieve chemical accuracy compared to experimental references, and well inside the scatter of traditional density functional material modeling approaches. This pushes the state of the art for simulation of molecular adsorption on surfaces, and marks progress into a post-density functional era for more reliable and improvable approaches to first-principles modeling of surface problems at an unprecedented scale and accuracy using ab-initio quantum many-body methods.

cond-mat.mtrl-sci↗

arXiv2024

Superconductivity in nickelate and cuprate superconductors with strong bilayer coupling

The discovery of superconductivity at 80 K under high pressure in La$_3$Ni$_2$O$_7$ presents the groundbreaking confirmation that high-$T_c$ superconductivity is a property of strongly correlated materials beyond cuprates. We use density functional theory (DFT) calculations of the band structure of La$_3$Ni$_2$O$_7$ under pressure to verify that the low-energy bands are composed almost exclusively of Ni 3$d_{x^2-y^2}$ and O 2$p$ orbitals. We deduce that the Ni 3$d_{z^2}$ orbitals are essentially decoupled by the geometry of the high-pressure structure and by the effect of the Ni Hund coupling being strongly suppressed, which results from the enhanced interlayer antiferromagnetic interaction between $d_{z^2}$ orbitals and the strong intralayer hybridization of the $d_{x^2-y^2}$ orbitals with O 2$p$. By introducing a tight-binding model for the Fermi surfaces and low-energy dispersions, we arrive at a bilayer $t$-$t_\perp$-$J$ model with strong interlayer hopping, which we show is a framework unifying La$_3$Ni$_2$O$_7$ with cuprate materials possessing similar band structures, particularly the compounds La$_2$CaCu$_2$O$_6$, Pb$_2$Sr$_2$YCu$_3$O$_8$, and EuSr$_2$Cu$_2$NbO$_8$. We use a renormalized mean-field theory to show that these systems should have ($d$+$is$)-wave superconductivity, with a dominant $d$-wave component and the high $T_c$ driven by the near-optimally doped $β$ band, while the $α$ band adds an $s$-wave component that should lead to clear experimental signatures.

cond-mat.supr-con↗

arXiv2024

GPU-accelerated Auxiliary-field quantum Monte Carlo with multi-Slater determinant trial states

The accuracy of phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) can be systematically improved with better trial states. Using multi-Slater determinant trial states, ph-AFQMC has the potential to faithfully treat strongly correlated systems, while balancing the static and dynamical correlations on an equal footing. This preprint presents an implementation and application of graphics processing unit-accelerated ph-AFQMC, for multi-Slater determinant trial wavefunctions (GPU-accelerated MSD-AFQMC), to enable efficient simulation of large-scale, strongly correlated systems. This approach allows for nearly-exact computation of ground state energies in multi-reference systems. Our GPU-accelerated MSD-AFQMC is implemented in the open-source code \texttt{ipie}, a Python-based AFQMC package [\textit{J. Chem. Theory Comput.}, 2022, 19(1): 109-121]. We benchmark the performance of the GPU code on transition-metal clusters like [Cu$_2$O$_2$]$^{2+}$ and [Fe$_2$S$_2$(SCH$_3$)]$^{2-}$. The GPU code achieves at least sixfold speedup in both cases, comparing the timings of a single A100 GPU to that of a 32-CPU node. For [Fe$_2$S$_2$(SCH$_3$)]$^{2-}$, we demonstrate that our GPU MSD-AFQMC can recover the dynamical correlation necessary for chemical accuracy with an MSD trial, despite the large number of determinants required ($>10^5$). Our work significantly enhances the efficiency of MSD-AFQMC calculations for large, strongly correlated molecules by utilizing GPUs, offering a promising path for exploring the electronic structure of transition metal complexes.

physics.chem-ph↗

arXiv2023

Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions

The computational demand posed by applying multi-Slater determinant trials in phaseless auxiliary-field quantum Monte Carlo methods (MSD-AFQMC) is particularly significant for molecules exhibiting strong correlations. Here, we propose using direct-product wave functions as trials for MSD-AFQMC, aiming to reduce computational overhead by leveraging the compactness of multi-Slater determinant trials in direct-product form (DP-MSD). This efficiency arises when the active space can be divided into non-coupling subspaces, a condition we term ``decomposable active space''. By employing localized-active space self-consistent field wave functions as an example of such trials, we demonstrate our proposed approach across a range of molecular systems, each exhibiting varying degrees of complexity in their electronic structures. Our findings indicate that the compact DP-MSD trials can reduce computational costs substantially, by up to 36 times for the C2H6N4 molecule where the two double bonds between nitrogen N=N are clearly separated by a C-C single bond, while maintaining accuracy when active spaces are decomposable. In the case of larger systems such as the benzene dimer, characterized by weak coupling between the two monomers, we observed a decrease in computational cost compared to using a complete active space trial, yet we retained the same level of accuracy. However, for systems where these active subspaces strongly couple, a scenario we refer to as "strong subspace coupling", the method's accuracy decreases compared to that achieved with a complete active space approach. We anticipate that our method will be beneficial for systems with non-coupling to weakly-coupling subspaces that require local multireference treatments.

physics.chem-ph↗