arXiv subjects

Xiaodong Xing

Publications and source records attributed to Xiaodong Xing.

7 recordsLinked to original sources

arXiv2026

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data

Quantifying hemodynamics in the curved segments of the intracranial internal carotid artery is a core challenge in diagnosing vascular stenosis. Conventional full-field imaging, such as 4D Flow MRI, is costly and difficult to widely promote. Meanwhile, reconstructing full-field fluid information from easily accessible and non-invasive sparse measurement data (such as transcranial Doppler ultrasound/computed tomography angiography) is essentially a highly challenging ill-posed inverse problem. To overcome the severe optimization difficulties and generalization failures of conventional physics-informed neural networks (PINNs) in highly tortuous geometries, we propose a dual-correction physics-informed neural network (DCP-INN) framework taking into account a causal decoupling strategy. The proposed DCP-INN model utilizes a diamond-shaped main network to capture low-frequency trends in physical evolution, and employs a parallel wide-deep correction network to compensate for high-frequency residuals resulting from complex geometric shapes. Furthermore, the framework introduces a high-order physical loss function based on Taylor expansion to enhance local continuity under extremely sparse data constraints. To validate the proposed method, we performed computational evaluations on realistic vascular geometries with significant tortuosity. The results demonstrate that the method effectively mitigates optimization challenges and significantly reduces flow field reconstruction error. This study not only achieves physically credible and robust flow field reconstruction in complex morphologies but also provides a highly promising algorithmic foundation for building low-cost, high-resolution personalized cardiovascular digital twins in future.

physics.med-ph↗

arXiv2025

A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction

We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consists of two branches: spatial processing and time evolution. Regarding the spatial processing, the classical module encodes the local topological features of the road network based on the GCN method, and the quantum module is designed to map graph features onto parameterized quantum circuits through a differentiable pooling layer. The time evolution is solved by integrating multi-source contextual information and capturing dynamic trip dependencies on the classical TCN theory. Finally, our experimental results demonstrate that the proposed algorithm outperforms the current methods in terms of prediction accuracy and stability, validating the unique advantages of the quantum-enhanced mechanism in capturing high-dimensional spatial dependencies.

quant-ph↗

arXiv2025

Channel-Constrained Markovian Quantum Diffusion Model from Open System Perspective

We present a channel-constrained Markovian quantum diffusion (CCMQD) model that prepares quantum states by rigorously framing the generative process within the dynamics of open quantum systems. Our model interprets the forward diffusion process as natural decoherence using quantum master equations, whereas the reverse denoising is achieved by learning inverse quantum channels. Our core innovation is a comprehensive channel-constrained framework: we model the diffusion and denoising steps as quantum channels defined by Kraus operators, ensure their physical validity through optimization on the Stiefel manifold, and introduce tailored training strategies and loss functions that leverage this constrained structure for high-fidelity state reconstruction. Experimental validation on systems ranging from single qubits to entangled states $7$ -qubits demonstrates high-fidelity state generation, achieving fidelities exceeding $0.998$ under both random and depolarizing noise conditions. This work confirms that quantum diffusion can be characterized as a controlled Markov evolution, demonstrating that environmental interactions are not limited to being a source of decoherence but can also be utilized to achieve high-fidelity quantum state synthesis.

quant-ph↗

arXiv2024

Competing excitation quenching and charge exchange in ultracold Li-Ba$^+$ collisions

Hybrid atom-ion systems are a rich and powerful platform for studying chemical reactions, as they feature both excellent control over the electronic state preparation and readout as well as a versatile tunability over the scattering energy, ranging from the few-partial wave regime to the quantum regime. In this work, we make use of these excellent control knobs, and present a joint experimental and theoretical study of the collisions of a single $^{138}$Ba$^+$ ion prepared in the $5d\,^2D_{3/2,5/2}$ metastable states with a ground state $^6$Li gas near quantum degeneracy. We show that in contrast to previously reported atom-ion mixtures, several non-radiative processes, including charge exchange, excitation exchange and quenching, compete with each other due to the inherent complexity of the ion-atom molecular structure. We present a full quantum model based on high-level electronic structure calculations involving spin-orbit couplings. Results are in excellent agreement with observations, highlighting the strong coupling between the internal angular momenta and the mechanical rotation of the colliding pair, which is relevant in any other hybrid system composed of an alkali-metal atom and an alkaline-earth ion.

quant-ph↗

arXiv2023

Nuclear spin relaxation in cold atom-molecule collisions

We explore the quantum dynamics of nuclear spin relaxation in cold collisions of $^1Σ^+$ molecules with structureless atoms in an external magnetic field. To this end, we develop a rigorous coupled-channel methodology, which accounts for rotational and nuclear spin degrees of freedom of $^1Σ^+$ molecules, their interaction with an external magnetic field, as well as for anisotropic atom-molecule interactions. We apply the methodology to study collisional relaxation of the nuclear spin sublevels of $^{13}$CO molecules immersed in a cold buffer gas of $^4$He atoms. We find that nuclear spin relaxation in the ground rotational manifold of CO occurs extremely slowly due to the absence of direct couplings between the nuclear spin sublevels. The rates of collisional transitions between the $N=1$ nuclear spin states of CO are generally much higher due to the direct nuclear spin-rotation coupling between the states. These transitions obey selection rules, which depend on the values of space-fixed projections of rotational and nuclear spin angular momenta for the initial and final molecular states. For some initial states, we also observe a strong magnetic field dependence, which can be understood using the first Born approximation. We use our calculated nuclear spin relaxation rates to investigate the thermalization of a single nuclear spin state of CO$(N=0)$ immersed in a cold buffer gas of He. The calculated nuclear spin relaxation times ($T_1\simeq 0.5$ s at $T=1$ K) display a steep temperature dependence decreasing rapidly at elevated temperatures due to the increased population of rotationally excited states, which undergo nuclear spin relaxation at a much faster rate. Thus, long relaxation times of $N=0$ nuclear spin states in cold collisions with buffer gas atoms can only be maintained at sufficiently low temperatures ($kT\ll 2B_e$), where $B_e$ is the rotational constant.

physics.chem-ph↗

arXiv2023

A hybrid quantum-classical algorithm for multichannel quantum scattering of atoms and molecules

We propose a hybrid quantum-classical algorithm for solving the time-independent Schrödinger equation for atomic and molecular collisions. The algorithm is based on the $S$-matrix version of the Kohn variational principle, which computes the fundamental scattering $S$-matrix by inverting the Hamiltonian matrix expressed in the basis of square-integrable functions. The computational bottleneck of the classical algorithm -- symmetric matrix inversion -- is addressed here using the variational quantum linear solver (VQLS), a recently developed noisy intermediate-scale quantum (NISQ) algorithm for solving systems of linear equations. We apply our algorithm to single and multichannel quantum scattering problems, obtaining accurate vibrational relaxation probabilities in collinear atom-molecule collisions. We also show how the algorithm could be scaled up to simulate collisions of large polyatomic molecules. Our results demonstrate that it is possible to calculate scattering cross sections and rates for complex molecular collisions on NISQ quantum processors, opening up the possibility of scalable digital quantum computation of gas-phase bimolecular collisions and reactions of relevance to astrochemistry and ultracold chemistry.

quant-ph↗

arXiv2022

Ion loss events in a cold Rb-Ca$^+$ hybrid trap: photodissociation, black-body radiation and non-radiative charge exchange

We theoretically investigate the collisional dynamics of laser-cooled $^{87}$Rb ground-state atoms and $^{40}$Ca$^+$ ground-state ions in the context of the hybrid trap experiment of Ref. [Phys. Rev. Lett. 107, 243202 (2011)], leading to ion losses. Cold $^{87}$Rb$^{40}$Ca$^+$ ground-state molecular ions are created by radiative association, and we demonstrate that they are protected against photodissociation by black-body radiation and by the $^{40}$Ca$^+$ cooling laser at 397~nm. This study yields an interpretation of the direct observation of $^{87}$Rb$^{40}$Ca$^+$ ions in the experiment, in contrast to other hybrid trap experiments using other species. Based on novel molecular data for the spin-orbit interaction, we also confirm that the non-radiative charge-exchange is the dominant loss process for Ca$^+$ and obtain rates in agreement with experimental observations and a previous calculation.

physics.chem-ph↗