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Near-term quantum devices generally suffer from shallow circuit depth and hence limited expressivity due to noise and decoherence. To address this, we propose tensor-network-assisted parametrized quantum circuits, which concatenate a…

Quantum Physics · Physics 2023-12-01 Junxiang Huang , Wenhao He , Yukun Zhang , Yusen Wu , Bujiao Wu , Xiao Yuan

Nonequilibrium dynamics of quantum many-body systems is challenging for classical computing, providing opportunities for demonstrating practical quantum computational advantage with analogue quantum simulators. Owing to the intimate…

Recently the use of neural networks has been introduced in the context of the signed particle formulation of quantum mechanics to rapidly and reliably compute the Wigner kernel of any provided potential. This new technique has introduced…

Computational Physics · Physics 2018-06-04 Jean Michel Sellier , Jacob Leygonie , Gaetan Marceau Caron

The use of Neural Networks in quantum many-body theory has seen a formidable rise in recent years. Among the many possible applications, one surely is to make use of their pattern recognition power when dealing with the study of equilibrium…

Strongly Correlated Electrons · Physics 2024-12-04 Filippo Caleca , Simone Tibaldi , Elisa Ercolessi

In this paper we develop a novel method to solve problems involving quantum optical systems coupled to coherent quantum feedback loops featuring time delays. Our method is based on exact mappings of such non-Markovian problems to equivalent…

Quantum Physics · Physics 2023-11-14 Kseniia Vodenkova , Hannes Pichler

Classical molecular dynamics (MD) is a well established and powerful tool in various fields of science, e.g. chemistry, plasma physics, cluster physics and condensed matter physics. Objects of investigation are few-body systems and…

Strongly Correlated Electrons · Physics 2016-02-03 Vladimir Filinov , Michael Bonitz , Alexei Filinov , Volodymyr Golubnychiy

The efficient simulation of complex quantum systems remains a central challenge due to the exponential growth of Hilbert space with system size. Tensor network methods have long been established as powerful approximation schemes, and their…

Computational Physics · Physics 2026-03-16 Min Chen , Minzhao Liu , Changhun Oh , Liang Jiang , Yuri Alexeev , Junyu Liu

Finding the distribution of the velocities and pressures of a fluid by solving the Navier-Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of…

Machine Learning · Computer Science 2024-07-16 Alexandr Sedykh , Maninadh Podapaka , Asel Sagingalieva , Karan Pinto , Markus Pflitsch , Alexey Melnikov

In recent years, the infinite time-evolution block decimation (iTEBD) method has been demonstrated to be one of the most efficient and powerful numerical schemes for time-evolution in one-dimensional quantum many-body systems. However, a…

Strongly Correlated Electrons · Physics 2020-07-15 Tomohiro Hashizume , Jad C. Halimeh , Ian P. McCulloch

Recently developed quantum algorithms suggest that quantum computers can solve certain problems and perform certain tasks more efficiently than conventional computers. Among other reasons, this is due to the possibility of creating…

Quantum Physics · Physics 2007-05-23 Rolando D. Somma

Quantum many-body devices suffer from imperfections that destabilize dynamics and limit scalability. We show that the dynamical growth of entanglement can intrinsically protect generic quantum dynamics against coherent and perturbative…

Quantum Physics · Physics 2026-02-25 Tianfeng Feng , Yue Cao , Wenjun Yu , Junkai Zeng , Xiaopeng Li , Xiu-Hao Deng , Qi Zhao

Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constraint losses during the model training process. However, one…

Machine Learning · Computer Science 2024-02-06 Hao Zhou , Sibo Cheng , Rossella Arcucci

Quantum simulators have made a remarkable progress towards exploring the dynamics of many-body systems, many of which offer a formidable challenge to both theoretical and numerical methods. While state-of-the-art quantum simulators are in…

The development of powerful numerical techniques has drastically improved our understanding of quantum matter out of equilibrium. Inspired by recent progress in the area of noisy intermediate-scale quantum devices, this paper highlights…

Quantum Physics · Physics 2024-04-01 Jonas Richter

This study established a quantum-classical hybrid framework that integrates quantum computing paradigm with meshfree finite particle method. By harnessing quantum superposition and entanglement, it hybridized the critical computational…

Fluid Dynamics · Physics 2025-09-16 Yudong Li , Wenkui Shi , Yan Li , Chunfa Wang , Ling Tao , Zhuojia Fu , Moubin Liu , Zhiqiang Feng

Interactions between particles are usually a resource for quantum computing, making quantum many-body systems intractable by any known classical algorithm. In contrast, noise is typically considered as being inimical to quantum many-body…

Quantum Physics · Physics 2021-10-29 Oles Shtanko , Abhinav Deshpande , Paul S. Julienne , Alexey V. Gorshkov

Studies of strongly nonlinear dynamical systems such as turbulent flows call for superior computational prowess. With the advent of quantum computing, a plethora of quantum algorithms have demonstrated, both theoretically and…

Quantum Physics · Physics 2025-04-30 Sachin S. Bharadwaj , Katepalli R. Sreenivasan

Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems.…

Quantum Physics · Physics 2025-03-31 Zheng An , Jiahui Wu , Zidong Lin , Xiaobo Yang , Keren Li , Bei Zeng

Quantum computing gives direct access to the study of real-time dynamics of quantum many-body systems. In principle, it is possible to directly calculate non-equal-time correlation functions, from which one can detect interesting phenomena,…

Whilst the partial differential equations that govern the dynamics of our world have been studied in great depth for centuries, solving them for complex, high-dimensional conditions and domains still presents an incredibly large…

Machine Learning · Computer Science 2023-03-07 Edward Small
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