arXiv subjects

Qitong Hu

Publications and source records attributed to Qitong Hu.

6 recordsLinked to original sources

arXiv2026

Quantum Implicit-Explicit Schemes for Multiscale Ordinary and Partial Differential Equations via Schr\"odingerization

In this paper, we present a quantum implicit-explicit (IMEX) scheme for multiscale ordinary and partial differential equations whose discretization parameters are independent of the scaling parameter $\varepsilon$. A key ingredient of our approach is a continuous-time formulation of classical IMEX schemes, which decouples the evolution time of the quantum algorithm from the physical time of the differential equation and is therefore particularly useful in multiscale settings. Building on this idea, we employ the Schr\"odingerization framework [Phys. Rev. Lett. 133 (2024), 230602] to implement IMEX schemes on quantum computers. Compared to previous HHL type quantum AP scheme [J. Comput. Phys. 471 (2022), 111641], this new method requires narrower -- an extra logarithmic factor -- auxiliary register numerical examples on linear heat and multiscale telegraph equations demonstrate the independence in $\varepsilon$ of the method.

math.NA↗

arXiv2026

Quantum Simulation of Non-Unitary Dynamics via Amplitude-Phase Separation

Linear non-unitary dynamics arise in open quantum systems, non-Hermitian models, and numerical evolution problems, yet current quantum algorithms do not cleanly separate coherent and dissipative effects at the design level. We introduce Amplitude-Phase Separation (APS), a decomposition framework with two complementary forms: phase-driven APS isolates the unitary component and maps the remainder to a Hermitian problem, whereas amplitude-driven APS extracts the Hermitian component and treats the remaining interaction separately. For time-independent dynamics, the two routes capture complementary advantages within one framework: phase-driven APS yields additive rather than multiplicative tolerance dependence, while amplitude-driven APS yields square-root dissipative scaling in multiscale regimes. APS also provides a unified interpretation of representative methods, including LCHS (Linear Combination of Hamiltonian Simulation) and NDME (Non-Diagonal Density Matrix Encoding), and clarifies where coherent and dissipative bottlenecks enter non-unitary simulation. The benchmarks confirm the predicted crossover between phase-driven and amplitude-driven advantages in advection-diffusion and Bloch-relaxation models.

quant-ph↗

arXiv2025

Quantum Algorithms for Solving Generalized Linear Systems via Momentum Accelerated Gradient and Schr\"odingerization

In this paper, we propose a quantum algorithm that combines the momentum accelerated gradient method with Schr\"odingerization [S. Jin, N. Liu and Y. Yu, Phys. Rev. Lett, 133 (2024), 230602][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603], achieving polynomial speedup over its classical counterpark in solving linear systems. The algorithm achieves a query complexity of the same order as the Schr\"odingerization based damped dynamical system method, namely, linear dependence on the condition number of the matrix, and can overcome the practical limitations of existing non-Schr\"odingerization-based quantum linear system algorithms. These limitations stem from their reliance on techniques such as VTAA and RM, which introduce substantial quantum hardware resource overhead. Furthermore, it demonstrates both theoretically and experimentally that the auxiliary variables introduced by our method do not dominate the error reduction at any point, thereby preventing a significant increase in the actual evolution time compared to the theoretical prediction. In contrast, the damped method fails to meet this criterion. This gives new perspectives for quantum algorithms for linear systems, establishing a novel analytical framework for algorithms with broader applicability, faster convergence rates, and superior solution quality.

quant-ph↗

arXiv2024

Key motifs searching in complex dynamical systems

Key network motifs searching in complex networks is one of the crucial aspects of network analysis. There has been a series of insightful findings and valuable applications for various scenarios through the analysis of network structures. However, in dynamic systems, slight changes in the choice of dynamic equations and parameters can alter the significance of motifs. The known methods are insufficient to address this issue effectively. In this paper, we introduce a concept of perturbation energy based on the system's Jacobian matrix, and define motif centrality for dynamic systems by seamlessly integrating network topology with dynamic equations. Through simulations, we observe that the key motifs obtained by the proposed energy method present better effective and accurate than them by integrating network topology methods, without significantly increasing algorithm complexity. The finding of key motifs can be used to apply for system control, such as formulating containment policies for the spread of epidemics and protecting fragile ecosystems. Additionally, it makes substantial contribution to a deeper understanding of concepts in physics, such as signal propagation and system's stability.

physics.soc-ph↗

arXiv2023

Fundamental Patterns of Signal Propagation in Complex Networks

Various disasters stem from minor perturbations, such as the spread of infectious diseases, cascading failure in power grids, etc. Analyzing perturbations is crucial for both theoretical and application fields. Previous researchers have proposed basic propagation patterns for perturbation and explored the impact of basic network motifs on the collective response to these perturbations, However, the current framework is limited in its ability to decouple interactions, and therefore cannot analyze more complex structures. In this article, we establish an effective, robust and powerful propagation framework under a general dynamic model. This framework reveals common and dense network motifs that exert a critical influence on signal propagation, often spanning orders of magnitude compared with conclusions generated by previous work. Moreover, our framework provides a new approach to understand the fundamental principles of complex systems and the negative feedback mechanism, which is of great significance for research of system controlling and network resilience.

physics.soc-ph↗

arXiv2020

The effect of interurban movements on the spatial distribution of population in China

Understanding how interurban movements can modify the spatial distribution of the population is important for transport planning but is also a fundamental ingredient for epidemic modeling. We focus here on vacation trips (for all transportation modes) during the Chinese Lunar New Year and compare the results for 2019 with the ones for 2020 where travel bans were applied for mitigating the spread of a novel coronavirus (COVID-19). We first show that these travel flows are broadly distributed and display both large temporal and spatial fluctuations, making their modeling very difficult. When flows are larger, they appear to be more dispersed over a larger number of origins and destinations, creating de facto hubs that can spread an epidemic at a large scale. These movements quickly induce (in about a week) a very strong population concentration in a small set of cities. We characterize quantitatively the return to the initial distribution by defining a pendular ratio which allows us to show that this dynamics is very slow and even stopped for the 2020 Lunar New Year due to travel restrictions. Travel restrictions obviously limit the spread of the diseases between different cities, but have thus the counter-effect of keeping high concentration in a small set of cities, a priori favoring intra-city spread, unless individual contacts are strongly limited. These results shed some light on how interurban movements modify the national distribution of populations, a crucial ingredient for devising effective control strategies at a national level.

physics.soc-ph↗