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Based on our earlier works [Phys. Rev. B 75, 195127 (2007) & J. Chem. Phys. 128, 234703 (2008)], we propose a formally exact and numerically convenient approach to simulate time-dependent quantum transport from first-principles. The…

Chemical Physics · Physics 2015-05-18 Xiao Zheng , GuanHua Chen , Yan Mo , SiuKong Koo , Heng Tian , ChiYung Yam , YiJing Yan

Optical techniques have been employed to coherently control the quantum transport through nanojunctions. Conventional works on optical control of quantum transport usually applied a tailored electrical pulses to perform specific tasks. In…

Mesoscale and Nanoscale Physics · Physics 2015-10-13 Yu Zhang

Many causal and structural parameters in economics can be identified and estimated by computing the value of an optimization program over all distributions consistent with the model and the data. Existing tools apply when the data is…

Econometrics · Economics 2025-07-31 Andrei Voronin

The theory of optimal transport of probability measures has wide-ranging applications across a number of different fields, including concentration of measure, machine learning, Markov chains, and economics. The generalisation of optimal…

Quantum Physics · Physics 2026-04-21 Emily Beatty

We design optimal harmonic-trap trajectories to transport cold atoms without final excitation, combining an inverse engineering techniqe based on Lewis-Riesenfeld invariants with optimal control theory. Since actual traps are not really…

Quantum Physics · Physics 2011-11-08 Xi Chen , E. Torrontegui , Dionisis Stefanatos , Jr-Shin Li , J. G. Muga

The objective in statistical Optimal Transport (OT) is to consistently estimate the optimal transport plan/map solely using samples from the given source and target marginal distributions. This work takes the novel approach of posing…

Machine Learning · Computer Science 2020-11-11 J. Saketha Nath , Pratik Jawanpuria

We consider the rate-limited quantum-to-classical optimal transport in terms of output-constrained rate-distortion coding for both finite-dimensional and continuous-variable quantum-to-classical systems with limited classical common…

Quantum Physics · Physics 2023-11-30 Hafez M. Garmaroudi , S. Sandeep Pradhan , Jun Chen

We reassess the concept of transition at minimum work in classical stochastic finite-time thermodynamics, when the system dynamics is modelled by a diffusion process. We show that a well-posed formulation of the optimal control problem…

Statistical Mechanics · Physics 2026-03-13 Paolo Muratore-Ginanneschi , Julia Sanders

Information processing machines at the nanoscales are unavoidably affected by thermal fluctuations. Efficient design requires understanding how nanomachines can operate at minimal energy dissipation. In this letter we focus on mechanical…

Statistical Mechanics · Physics 2015-03-17 Paolo Muratore-Ginanneschi , Kay Schwieger

Information processing typically occurs via the composition of modular units, such as universal logic gates. The benefit of modular information processing, in contrast to globally integrated information processing, is that complex global…

Statistical Mechanics · Physics 2018-08-08 Alexander B. Boyd , Dibyendu Mandal , James P. Crutchfield

Understanding the ultimate rate at which information propagates is a pivotal issue in nonequilibrium physics. Nevertheless, the task of elucidating the propagation speed inherent in quantum bosonic systems presents challenges due to the…

Quantum Physics · Physics 2024-10-01 Tan Van Vu , Tomotaka Kuwahara , Keiji Saito

Second law of thermodynamics can be apparently violated for systems whose dynamics depends on acquired information by measurement. However, when one consider measurement and erasure process together along with the system it saves the second…

Statistical Mechanics · Physics 2016-11-08 Shubhashis Rana , A. M. Jayannavar

We study the Bergman determinantal point process from a theoretical point of view motivated by its simulation. We construct restricted and restricted-truncated variants of the Bergman kernel and show optimal transport inequalities involving…

Probability · Mathematics 2026-03-09 William Driot , Laurent Decreusefond

Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation…

Machine Learning · Statistics 2014-03-25 Marco Cuturi

Phase transitions impose topological constraints on thermodynamic state variables, masking energetic fluctuations at the phase boundary. This constraint is most apparent in melting systems, where temperature remains pinned despite continued…

Atmospheric and Oceanic Physics · Physics 2026-02-09 Zhiang Xie

Thermodynamics and information have intricate interrelations. Often thermodynamics is considered to be the logical premise to justify that information is physical - through Landauer's principle -, thereby also linking information and…

A fundamental result of thermodynamic geometry is that the optimal, minimal-work protocol that drives a nonequilibrium system between two thermodynamic states in the slow-driving limit is given by a geodesic of the friction tensor, a…

Statistical Mechanics · Physics 2024-10-24 Adrianne Zhong , Michael R. DeWeese

Classical optimal transport problem seeks a transportation map that preserves the total mass betwenn two probability distributions, requiring their mass to be the same. This may be too restrictive in certain applications such as color or…

Machine Learning · Statistics 2020-06-15 Laetitia Chapel , Mokhtar Z. Alaya , Gilles Gasso

Starting from Landauer's slogan "information is physical," we revise and modify Landauer's principle stating that the erasure of information has a minimal price in the form of a certain quantity of free energy. We establish a direct link…

Quantum Physics · Physics 2019-11-20 Ämin Baumeler , Stefan Wolf

Discriminating between distributions is an important problem in a number of scientific fields. This motivated the introduction of Linear Optimal Transportation (LOT), which embeds the space of distributions into an $L^2$-space. The…

Machine Learning · Statistics 2021-05-27 Caroline Moosmüller , Alexander Cloninger