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Related papers: Memory Kernel Coupling Theory: Obtain Time Correla…

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Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge…

Quantum Physics · Physics 2019-12-18 X. -D. Cai , D. Wu , Z. -E. Su , M. -C. Chen , X. -L. Wang , L. Li , N. -L. Liu , Chao-Yang Lu , Jian-Wei Pan

Based on Sj\"{o}gren und Sjolander's Mode-Coupling(MC)-Model, we have reformulated and calculated the memory kernel (MK) of the velocity autocorrelation function (VACF)on a Ni$_{0.2}$Zr$_{0.8}$-liquid. Reformulating means here that we have…

Statistical Mechanics · Physics 2007-05-23 A. B. Mutiara

The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges such as high computational cost and memory usage. Researchers are…

Quantum Physics · Physics 2026-03-24 Yuichi Kamata , Quoc Hoan Tran , Yasuhiro Endo , Hirotaka Oshima

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent…

Neurons and Cognition · Quantitative Biology 2023-10-27 Mufeng Tang , Helen Barron , Rafal Bogacz

With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum…

Quantum Physics · Physics 2021-04-20 Maria Schuld

We consider the correlation function of an arbitrary number of local observables in quantum field theory. We show that, at tree level in the strong field regime, these correlations arise solely from fluctuations in the initial state. We…

High Energy Physics - Theory · Physics 2017-10-25 Francois Gelis

Memory effects arise in many complex systems, from protein folding, to the spreading of epidemics and financial decisions. While so-called non-Markovian dynamics is common in larger systems with interacting components, observations in…

This study addresses the problem of convolutional kernel learning in univariate, multivariate, and multidimensional time series data, which is crucial for interpreting temporal patterns in time series and supporting downstream machine…

Machine Learning · Computer Science 2025-04-17 Xinyu Chen , HanQin Cai , Fuqiang Liu , Jinhua Zhao

The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century. The state-of-the-art methods are parametric methods and…

Systems and Control · Electrical Eng. & Systems 2024-10-29 Xiaozhu Fang , Biqiang Mu , Tianshi Chen

Many recent advancements in quantum computing leverage strong drives on nonlinear systems for state preparation, signal amplification, or gate operation. However, the interplay within such strongly driven system introduces multi-scale…

Quantum Physics · Physics 2024-08-02 Leon Bello , Wentao Fan , Aditya Gandotra , Hakan E. Türeci

The many-fingered time (MFT) formulation of many-particle quantum mechanics and quantum field theory is a natural framework that overcomes the problem of "instantaneous collapse" in entangled systems that exhibit nonlocalities. The…

Quantum Physics · Physics 2007-05-23 H. Nikolic

Out-of-time-order correlations (OTOCs) characterize the scrambling, or delocalization, of quantum information over all the degrees of freedom of a system and thus have been proposed as a proxy for chaos in quantum systems. Recent…

Quantum Physics · Physics 2018-01-30 Martin Gärttner , Philipp Hauke , Ana Maria Rey

The Coupled Cluster (CC) method is used to compute the electronic correlation energy in atoms and molecules and often leads to highly accurate results. However, due to its single-reference nature, standard CC in its projected form fails to…

Metal Organic Frameworks (MOFs) are promising materials to help mitigate the effects of global warming by selectively absorbing $\text{CO}_{2}$ for direct capture. Accurate quantum chemistry simulations are a useful tool to help select and…

We investigate quantum correlations in time in different approaches. We assume that temporal correlations should be treated in an even-handed manner with spatial correlations. We compare the pseudo-density matrix formalism with several…

Quantum Physics · Physics 2020-12-23 Tian Zhang , Oscar Dahlsten , Vlatko Vedral

This article presents a concrete mathematical framework for the generation of entangled quantum states from classical stochastic processes. We demonstrate that any density operator $\rho_{AB}$ of a composite system can be derived from the…

Quantum Physics · Physics 2026-01-27 Andrei Khrennikov

Every year, substantial theoretical and experimental progress is made towards the realisation of a genuinely new computational paradigm in the construction of a quantum computer. But progress is fractal; to make headway is to unearth the…

Quantum Physics · Physics 2024-05-10 Gregory A. L. White

The Motzkin and Fredkin quantum spin chains are described by frustration-free Hamiltonians recently introduced and studied because of their anomalous behaviors in the correlation functions and in the entanglement properties. In this paper…

Statistical Mechanics · Physics 2020-01-10 L. Dell'Anna , L. Barbiero , A. Trombettoni

In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic…

Disordered Systems and Neural Networks · Physics 2023-05-10 Anantha Rao , Stephen Carr , Charles Snider , D. E. Feldman , Chandrasekhar Ramanathan , V. F. Mitrović

An implementation of coupled-cluster (CC) theory to treat atoms and molecules in finite magnetic fields is presented. The main challenges stem from the magnetic-field dependence in the Hamiltonian, or, more precisely, the appearance of the…

Chemical Physics · Physics 2015-09-02 Stella Stopkowicz , Jürgen Gauss , Kai K. Lange , Erik I. Tellgren , Trygve Helgaker
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