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Related papers: Gauge P-representations for quantum-dynamical prob…

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Dealing with a generic time-local non-Markovian master equation, we define current and power to be process-dependent as in classical thermodynamics. Each process is characterized by a symmetry transformation, a gauge of the master equation,…

Quantum Physics · Physics 2023-08-01 Fernando Nicacio , Raphael N. P. Maia

In numerical studies of the dynamics of unbound quantum mechanical systems, absorbing boundary conditions are frequently applied. Although this certainly provides a useful tool in facilitating the description of the system, its applications…

Quantum Physics · Physics 2015-05-13 Sølve Selstø , Simen Kvaal

We discuss stochastic phase-space methods within the truncated Wigner approximation and show explicitly that they can be used to solve non-equilibrium dynamics of bosonic atoms in one-dimensional traps. We consider systems both with and…

Quantum Gases · Physics 2012-09-25 J. Ruostekoski , A. D. Martin

Thermodynamics is based on a coarse-grained approach, from which its fundamental variables emerge, effectively erasing the complicate details of the microscopic dynamics within a macroscopic system. The strength of Thermodynamics lies in…

Quantum Physics · Physics 2025-04-17 Gabriel Fernandez Ferrari , Łukasz Rudnicki , Lucas Chibebe Céleri

A method of quantizing parametrized systems is developed that is based on a kind of ``gauge invariant'' quantities---the so-called perennials (a perennial must also be an ``integral of motion''). The problem of time in its particular form…

General Relativity and Quantum Cosmology · Physics 2009-10-22 P. Hajicek

If a large Quantum Computer (QC) existed today, what type of physical problems could we efficiently simulate on it that we could not simulate on a classical Turing machine? In this paper we argue that a QC could solve some relevant physical…

Quantum Physics · Physics 2009-11-10 Rolando Somma , Gerardo Ortiz , Emanuel Knill , James Gubernatis

A universal quantum computer of large scale is not available yet, however, intermediate models of quantum computation would still permit demonstrations of a quantum computational advantage over classical computing and could challenge the…

Quantum Physics · Physics 2024-11-15 Raphael A. Abrahao , Arman Mansouri , Austin P. Lund

Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and struggle with localized, high-frequency structures while incurring quadratic cost in…

Machine Learning · Computer Science 2026-02-26 Zhihao Li , Yu Feng , Zhilu Lai , Wei Wang

Boson Sampling is a task that is conjectured to be computationally hard for a classical computer, but which can be efficiently solved by linear-optical interferometers with Fock state inputs. Significant advances have been reported in the…

Quantum Physics · Physics 2023-04-14 Nicolò Spagnolo , Daniel J. Brod , Ernesto F. Galvão , Fabio Sciarrino

Continuous-variable bosonic systems stand as prominent candidates for implementing quantum computational tasks. While various necessary criteria have been established to assess their resourcefulness, sufficient conditions have remained…

Quantum Physics · Physics 2024-10-08 Cameron Calcluth , Nicolas Reichel , Alessandro Ferraro , Giulia Ferrini

Boson Sampling represents a promising approach to obtain an evidence of the supremacy of quantum systems as a resource for the solution of computational problems. The classical hardness of Boson Sampling has been related to the so called…

Quantum Physics · Physics 2016-11-09 Ludovico Latmiral , Nicolò Spagnolo , Fabio Sciarrino

Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. However, existing GP approximations do not simultaneously support…

Machine Learning · Computer Science 2021-06-21 Will Tebbutt , Arno Solin , Richard E. Turner

We consider a quantization of relativistic wave equations which allows to treat quantum fields together with interacting particles at a finite time. We discuss also a dissipative interaction with the environment. We introduce a stochastic…

High Energy Physics - Theory · Physics 2007-05-23 Z. Haba

We present a highly efficient method for the numerical solution of coupled Gross-Pitaevskii equations describing the evolution dynamics of a multispecies mixture of Bose-Einstein condensates in time-dependent potentials. This method, based…

Approximation methods for calculating individual particle/ field motions in spacetime at the quantum level of accuracy (a key feature of the Bohm Picture of Quantum Mechanics (BP)), are studied. Modern textbook presentations of Quantum…

Nuclear Theory · Physics 2009-04-17 A. Miranda

We suggest a pseudospectral method for solving the three-dimensional time-dependent Gross-Pitaevskii (GP) equation and use it to study the resonance dynamics of a trapped Bose-Einstein condensate induced by a periodic variation in the…

Soft Condensed Matter · Physics 2025-10-20 Paulsamy Muruganandam , Sadhan K Adhikari

We show how to adapt the ideas of local energy and momentum conservation in order to derive modifications to the Gross-Pitaevskii equation which can be used phenomenologically to describe irreversible effects in a Bose-Einstein condensate.…

Condensed Matter · Physics 2007-05-23 C. W. Gardiner J. R. Anglin , T. I. A. Fudge

Gaussian unitaries, generated by quadratic Hamiltonians, are fundamental in quantum optics and continuous-variable computing. Their structures correspond to symplectic (bosons) and orthogonal (fermions) groups, but physical realizations…

Quantum Physics · Physics 2026-02-10 Jingqi Sun , Joshua Combes , Lucas Hackl

Gauge invariance of systems whose Hamilton-Jacobi equation is separable is improved by adding surface terms to the action fuctional. The general form of these terms is given for some complete solutions of the Hamilton-Jacobi equation. The…

General Relativity and Quantum Cosmology · Physics 2009-11-07 Hernan De Cicco , Claudio Simeone

Gaussian processes (GPs) are widely-used tools in spatial statistics and machine learning and the formulae for the mean function and covariance kernel of a GP $T u$ that is the image of another GP $u$ under a linear transformation $T$…

Probability · Mathematics 2024-10-08 Tadashi Matsumoto , T. J. Sullivan