arXiv subjects

Weitang Li

Publications and source records attributed to Weitang Li.

At least 19 recordsLinked to original sources

arXiv2026

Layer-Controlled Intermolecular Coupling and Many-Body Effects in C$_{60}$ Films

Crystalline C$_{60}$ is a molecular solid whose electronic properties emerge from the interplay of intermolecular hopping, electron correlations, and electron-vibration coupling. Unlike moir$\rm\acute{e}$ van der Waals heterostructures, where interaction strength is commonly tuned by twist angle, molecular materials offer a complementary route in which layer number, molecular orientation, and substrate registry provide experimentally accessible control parameters. Here we present a systematic thickness-dependent angle-resolved photoemission study of C$_{60}$ films, spanning the monolayer to the bulk limit. The HOMO-derived band exhibits a non-monotonic evolution: the intermediate-thickness film shows larger bandwidth, reduced effective mass, and pronounced gap-like and sub-band features. The experimental trends, together with Holstein-model simulations, point to strengthened effective intermolecular electronic coupling and enhanced electron-phonon-induced spectral renormalization in the intermediate-thickness regime. These results identify a dimensional crossover in C$_{60}$ films and establish layer number as an effective knob for engineering electronic structure and many-body interactions in molecular thin films.

cond-mat.mtrl-sci↗

arXiv2026

The Counterintuitive "One-Enhanced, One-Suppressed" Bifurcation Phenomenon of Spin Mobility under Weak Spin-Orbit Coupling in Helices

The chiral-induced spin selectivity (CISS) effect poses a longstanding puzzle: how weak spin-orbit coupling (SOC) within a helical molecule produces strong spin polarization. We uncover a counterintuitive phenomenon, a "one-enhanced, one-suppressed" bifurcation of spin-resolved mobility relative to the SOC-free case. We trace this phenomenon to an analytical sign reversal of spin-dependent transmission and identify it as the microscopic origin of CISS. The phenomenon is found absent in achiral systems and single-stranded DNA, validating our theoretical picture. Our findings resolve the core puzzle and establish the origin of CISS.

cond-mat.other↗

arXiv2026

Entanglement structure of the dynamical phases in the sub-Ohmic spin-boson model

The sub-Ohmic spin-boson model exhibits three distinct dynamical regimes in its spin population dynamics, classified as coherent, incoherent, and pseudo-coherent. Whether these regimes correspond to distinct spin-bath entanglement structures remains an open question. Here we address this using tree tensor network states with projector-splitting time evolution (TTN-TDVP-PS), scanning a broad grid in the sub-Ohmic $(s, \alpha)$ plane. We find that the spin entanglement entropy $S_\mathrm{spin}(t)$ reaches a stationary plateau on a timescale shorter than the polarization relaxation, enabling construction of a stationary entropy landscape from the stationary value $S_\mathrm{stable}$. Within this scalar entropy landscape, the entropy ridge broadly follows the population-based phase boundary at small $s$, but does not reproduce the two-branch structure at large $s$. The ridge remains single-valued within the incoherent region rather than separately tracking both population-based transitions. The Bloch-sphere representation provides a geometric interpretation of this behavior. The entropy plateau corresponds to trajectories settling onto constant-radius shells, with the ridge marking the parameters of smallest stationary Bloch radius. Mode-resolved bath entanglement shows that low-frequency modes dominate the environmental entropy scale and that coherent dynamics enhance bath-mode correlations beyond direct spin--mode correlations. These results establish the stationary spin entanglement entropy as a physically informative observable that complements population-based classifications of dissipative quantum dynamics.

quant-ph↗

arXiv2026

Automated Unitary Coupled Cluster Circuit Design via Differentiable Quantum Architecture Search

Designing compact and accurate circuits for the variational quantum eigensolver (VQE) is a central challenge in near-term quantum chemistry. Existing adaptive methods such as ADAPT-VQE design circuits by iteratively selecting operators from a predefined pool guided by gradient information and greedy heuristics. In this work, we adopt differentiable quantum architecture search (DQAS) as a circuit design framework based on the UCCSD operator pool, and introduce two complementary strategies: a global mode that simultaneously optimizes all operator selections, and a layerwise mode that constructs circuits incrementally while preserving previously learned structure. By relaxing discrete operator selection into a continuous differentiable optimization, DQAS enables gradient-based exploration over the combinatorial space of UCC circuit architectures. Benchmarks on BeH2, H4, LiH, H6, and H2O (8-14 qubits) show that both strategies achieve higher accuracy and fewer CNOT gates than ADAPT-VQE in the compact circuit regime, with up to 2.7-fold accuracy improvement for H2O and CNOT reductions of 13-17% at equivalent circuit depths. Benchmarks on the qubit-excitation-based (QEB) operator pool confirm that both advantages generalize beyond UCCSD. These results demonstrate that differentiable architecture search provides an effective and generalizable framework for designing accurate and compact VQE circuits in near-term quantum chemistry.

quant-ph↗

arXiv2026

TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCircuit-NG establishes a unified, tensor-native programming paradigm where quantum circuits, tensor networks, and neural networks fuse into a single, end-to-end differentiable computational graph. Built upon industry-standard machine learning backends (JAX, TensorFlow, PyTorch), the framework introduces comprehensive capabilities for approximate circuit simulation, analog dynamics, fermion Gaussian states, qudit systems, and scalable noise modeling. To tackle the exponential complexity of deep quantum circuits, TensorCircuit-NG implements advanced distributed computing strategies, including automated data parallelism and model-parallel tensor network slicing. We validate these capabilities on GPU clusters, demonstrating a near-linear speedup in distributed variational quantum algorithms. TensorCircuit-NG enables flagship applications, including end-to-end QML for CIFAR-100 computer vision, efficient pipelines from quantum states to neural networks via classical shadows, and differentiable optimization of tensor network states for many-body physics.

quant-ph↗

arXiv2026

Autonomous Quantum Simulation through Large Language Model Agents

We demonstrate that large language model (LLM) agents can autonomously perform tensor network simulations of quantum many-body systems, achieving approximately 90% success rate across representative benchmark tasks. Tensor network methods are powerful tools for quantum simulation, but their effective use requires expertise typically acquired through years of graduate training. By combining in-context learning with curated documentation and multi-agent decomposition, we create autonomous AI agents that can be trained in specialized computational domains within minutes. We benchmark three configurations (baseline, single-agent with in-context learning, and multi-agent with in-context learning) on problems spanning quantum phase transitions, open quantum system dynamics, and photochemical reactions. Systematic evaluation using DeepSeek-V3.2, Gemini 2.5 Pro, and Claude Opus 4.5 demonstrates that both in-context learning and multi-agent architecture are essential. Analysis of failure modes reveals characteristic patterns across models, with the multi-agent configuration substantially reducing implementation errors and hallucinations compared to simpler architectures.

quant-ph↗

arXiv2025

A Further Comparison of MPS and TTNS for Nonadiabatic Dynamics of Exciton Dissociation

Tensor networks, such as matrix product states (MPS) and tree tensor network states (TTNS), are powerful ans\"atze for simulating quantum dynamics. While both ans\"atze are theoretically exact in the limit of large bond dimensions, [J. Chem. Theory Comput. 2024, 20, 8767-8781] reported a non-negligible discrepancy in its calculations for exciton dissociation. To resolve this inconsistency, we conduct a systematic comparison using Renormalizer, a unified software framework for MPS and TTNS. By revisiting the benchmark P3HT:PCBM heterojunction model, we show that the observed discrepancies arise primarily from insufficient bond dimensions. By increasing bond dimensions, we reduce the relative difference in occupancy for weakly populated electronic states from up to 60% towards the end of the simulation to less than 10% and the absolute difference from 0.05 to 0.005. We also discuss the impact of tensor network structures on accuracy and efficiency, with the difference further reduced by an optimized TTNS structure. Our results confirm that both methods converge to numerically exact solutions when bond dimensions are adequately scaled. This work not only validates the reliability of both methods but also provides high-accuracy benchmark data for future developments in quantum dynamics simulations.

physics.chem-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

Multi-set variational quantum dynamics algorithm for simulating nonadiabatic dynamics on quantum computers

Accelerating quantum dynamical simulations with quantum computing has received considerable attention but remains a significant challenge. In variational quantum algorithms for quantum dynamics, designing an expressive and shallow-depth parameterized quantum circuit (PQC) is a key difficulty. Here, we proposed a multi-set variational quantum dynamics algorithm (MS-VQD) tailored for nonadiabatic dynamics involving multiple electronic states. MS-VQD employs multiple PQCs to represent the electronic-nuclear coupled wavefunction, with each circuit adapting to the motion of nuclear wavepacket on a specific potential energy surface. By simulating excitation energy transfer dynamics in molecular aggregates described by the Frenkel-Holstein model, we demonstrated that MS-VQD achieves the same accuracy as traditional VQD while requiring significantly shallower PQCs. Notably, its advantage increases with the number of electronic states, making it suitable for simulating nonadiabatic quantum dynamics in complex molecular systems.

physics.chem-ph↗

arXiv2025

Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction

Quantum computational chemistry holds great promise for simulating molecular systems more efficiently than classical methods by leveraging quantum bits to represent molecular wavefunctions. However, current implementations face significant limitations in accuracy due to hardware noise and algorithmic constraints. To overcome these challenges, we introduce a hybrid framework that learns molecular wavefunction using a combination of an efficient quantum circuit and a deep neural network. This approach enhances computational efficiency and accuracy, surpassing traditional quantum computational chemistry methods. Numerical benchmarking on molecular systems shows that our hybrid quantum-neural wavefunction approach achieves near-chemical accuracy, comparable to advanced quantum and classical techniques. Experimental validation on a superconducting quantum computer, using the isomerization reaction of cyclobutadiene, further demonstrates its practical applicability, with high accuracy in energy estimation and significant resilience to noise.

quant-ph↗

arXiv2024

Optimal Tree Tensor Network Operators for Tensor Network Simulations: Applications to Open Quantum Systems

Tree tensor network states (TTNS) decompose the system wavefunction to the product of low-rank tensors based on the tree topology, serving as the foundation of the multi-layer multi-configuration time-dependent Hartree (ML-MCTDH) method. In this work, we present an algorithm that automatically constructs the optimal and exact tree tensor network operators (TTNO) for any sum-of-product symbolic quantum operator.The construction is based on the minimum vertex cover of a bipartite graph. With the optimal TTNO, we simulate open quantum systems such as spin relaxation dynamics in the spin-boson model and charge transport in molecular junctions. In these simulations, the environment is treated as discrete modes and its wavefunction is evolved on equal footing with the system. We employ the Cole-Davidson spectral density to model the glassy phonon environment, and incorporate temperature effects via thermo field dynamics. Our results show that the computational cost scales linearly with the number of discretized modes, demonstrating the efficiency of our approach.

quant-ph↗

arXiv2024

Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz

The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from the previous iteration into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared to traditional optimization methods. Furthermore, numerical simulations up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.

physics.chem-ph↗

arXiv2024

A Hybrid Quantum Computing Pipeline for Real World Drug Discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

physics.chem-ph↗

arXiv2023

Roles of non-local electron-phonon coupling on the electrical conductivity and Seebeck coefficient: a TD-DMRG study

Organic molecular materials are potential high-performance thermoelectric materials. Theoretical understanding of thermoelectric conversion in organic materials is essential for rational molecular design for efficient energy conversion materials. In organic materials, non-local electron-phonon coupling plays a vital role in charge transport and leads to complex transport mechanisms, including hopping, phonon-assist current, band, and transient localization. In this work, based on time-dependent density matrix renormalization group (TD-DMRG) method, we look at the role of non-local electron-phonon coupling on the thermoelectric conversion in organic systems described by Holstein-Peierls model. We calculate the current-current correlation and the heat current-current correlation functions. We find that (i) non-local electron-phonon coupling has very weak influence on Seebeck coefficient because of the cancellation between heat current-current correlation function and the current-current correlation function, but it has strong influence on conductivity through dynamic disorders; (ii) doping concentration has a strong influence on both conductivity and Seebeck coefficient significantly, and the optimal doping ratio to reach the highest power factor is 3%-10% fillings.

cond-mat.mtrl-sci↗

arXiv2023

Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach

Achieving chemical accuracy with shallow quantum circuits is a significant challenge in quantum computational chemistry, particularly for near-term quantum devices. In this work, we present a Clifford-based Hamiltonian engineering algorithm, namely CHEM, that addresses the trade-off between circuit depth and accuracy. Based on variational quantum eigensolver and hardware-efficient ansatz, our method designs Clifford-based Hamiltonian transformation that (1) ensures a set of initial circuit parameters corresponding to the Hartree--Fock energy can be generated, (2) effectively maximizes the initial energy gradient with respect to circuit parameters, (3) imposes negligible overhead for classical processing and does not require additional quantum resources, and (4) is compatible with any circuit topology. We demonstrate the efficacy of our approach using a quantum hardware emulator, achieving chemical accuracy for systems as large as 12 qubits with fewer than 30 two-qubit gates. Our Clifford-based Hamiltonian engineering approach offers a promising avenue for practical quantum computational chemistry on near-term quantum devices.

quant-ph↗

arXiv2023

TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era

TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high performance on the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics. TenCirChem's capabilities are demonstrated through various examples, such as the calculation of the potential energy curve of $\textrm{H}_2\textrm{O}$ with a 6-31G(d) basis set using a 34-qubit quantum circuit, the examination of the impact of quantum gate errors on the variational energy of the $\textrm{H}_2$ molecule, and the exploration of the Marcus inverted region for charge transfer rate based on variational quantum dynamics. Furthermore, TenCirChem is capable of running real quantum hardware experiments, making it a versatile tool for both simulation and experimentation in the field of quantum computational chemistry.

quant-ph↗

arXiv2023

Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder

Digital quantum simulation of electron-phonon systems requires truncating infinite phonon levels into $N$ basis states and then encoding them with qubit computational basis. Unary encoding and the more compact binary/Gray encoding are the two most representative encoding schemes, which demand $\mathcal{O}(N)$ and $\mathcal{O}(\log{N})$ qubits as well as $\mathcal{O}(N)$ and $\mathcal{O}(N\log{N})$ quantum gates respectively. In this work, we propose a variational basis state encoding algorithm that reduces the scaling of the number of qubits and quantum gates to both $\mathcal{O}(1)$ for systems obeying the area law of entanglement entropy. The cost for the scaling reduction is a constant amount of additional measurement. The accuracy and efficiency of the approach are verified by both numerical simulation and realistic quantum hardware experiments. In particular, we find using one or two qubits for each phonon mode is sufficient to produce quantitatively correct results across weak and strong coupling regimes. Our approach paves the way for practical quantum simulation of electron-phonon systems on both near-term hardware and error-corrected quantum computers.

quant-ph↗

arXiv2022

Hybrid Quantum-Classical Boson Sampling Algorithm for Molecular Vibrationally Resolved Electronic Spectroscopy with Duschinsky Rotation and Anharmonicity

Using a photonic quantum computer for boson sampling has been demonstrated a tremendous advantage over classical supercomputers. It is highly desirable to develop boson sampling algorithms for realistic scientific problems. In this work, we propose a hybrid quantum-classical sampling (HQCS) algorithm to calculate the optical spectrum for complex molecules considering anharmonicity and Duschinsky rotation (DR) effects. The classical sum-over-state method for this problem has a computational complexity that exponentially increases with system size. In the HQCS algorithm, an intermediate harmonic potential energy surface (PES) is created, bridging the initial and final PESs. The magnitude and sign (-1 or +1) of the overlap between the initial state and the intermediate state are estimated by quantum boson sampling and by classical algorithms respectively, achieving an exponential speed-up. Additionally, the overlap between the intermediate state and the final state is efficiently evaluated by classical algorithms. The feasibility of HQCS is demonstrated in calculations of the emission spectrum of a Morse model as well as pyridine molecule by comparison with the nearly exact time-dependent density matrix renormalization group solutions. A near-term quantum advantage for realistic molecular spectroscopy simulation is proposed.

quant-ph↗