arXiv subjects

Yongchun Xu

Publications and source records attributed to Yongchun Xu.

4 recordsLinked to original sources

arXiv2026

Nonlinear path-following via the asymptotic numerical method on a quantum processor

Quantum computing offers a promising avenue for advancing computational methods in science and engineering. In this work, we introduce the quantum asymptotic numerical method (qANM), a framework for solving nonlinear path-following problems using quantum computing. Based on the principle of high-order perturbation techniques, the proposed method uses Taylor series expansions to transform complex nonlinear systems into sequences of linear equations, which are then solved using quantum linear solvers. The central objective of this study is to demonstrate nonlinear path-following with the required linear systems solved on real quantum hardware. To realize this objective, we develop the quantum-enhanced Jacobi method (q-Jacobi), an iterative quantum linear solver used in the hardware experiment. Numerical simulations on a quantum simulator validate the convergence of the method. A highlight of this work is a proof-of-principle experiment on a superconducting quantum processor. Despite the noise inherent in near-term quantum hardware, the experiment achieves 98% accuracy in tracking the nonlinear solution path. We believe this work provides a useful reference for applying quantum computing to nonlinear computational mechanics.

quant-ph↗

arXiv2025

Unraveling the Corrosion Mechanism of Boro-Alumino-Phospho-Silicate Glass: Advanced Insights from Solid-State NMR Spectroscopy

Corrosion mechanism of minerals and glass is a critical study domain in geology and materials science, vital for comprehending material durability under various environmental conditions. Despite decades of extensive study, a core aspect of these mechanisms - specifically, the formation of amorphous alteration layers upon exposure to aqueous environments - remains controversial. In this study, the corrosion behavior of a boro-alumino-phospho-silicate glass (BAPS) was investigated using advanced solid-state nuclear magnetic resonance (SSNMR) and SEM techniques. The results reveal a uniform nanoscale phase separation into Al-P-rich and Al-Si-rich domains. During corrosion, the Al-P-rich domain undergoes gelation, whereas the Al-Si-rich domain remains vitreous, forming a gel layer comprised of both phases. Although SEM images show a sharp gel/glass interface - suggestive of a dissolution-precipitation mechanism - the phase coexistence within the gel layer provides definitive evidence against such a mechanism. Instead, we propose an in situ transformation mechanism governed by chemical reactions, involving: (i) preferential hydrolysis of Al-P-rich domain leading to porous gel regions; (ii) retention of Al-Si glass domains within the gel layer, with water infiltrating inter-network spaces; and (iii) selective leaching of phosphorus over aluminum, leading to reorganization of the gel network.

cond-mat.mtrl-sci↗

arXiv2024

Quantum computing with error mitigation for data-driven computational homogenization

As a crossover frontier of physics and mechanics, quantum computing is showing its great potential in computational mechanics. However, quantum hardware noise remains a critical barrier to achieving accurate simulation results due to the limitation of the current hardware. In this paper, we integrate error-mitigated quantum computing in data-driven computational homogenization, where the zero-noise extrapolation (ZNE) technique is employed to improve the reliability of quantum computing. Specifically, ZNE is utilized to mitigate the quantum hardware noise in two quantum algorithms for distance calculation, namely a Swap-based algorithm and an H-based algorithm, thereby improving the overall accuracy of data-driven computational homogenization. Multiscale simulations of a 2D composite L-shaped beam and a 3D composite cylindrical shell are conducted with the quantum computer simulator Qiskit, and the results validate the effectiveness of the proposed method. We believe this work presents a promising step towards using quantum computing in computational mechanics.

cs.CE↗

arXiv2023

Quantum Computing Enhanced Distance-Minimizing Data-Driven Computational Mechanics

The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.

cs.CE↗