@article {3484, title = {Quantum thermodynamics of nonequilibrium processes in lattice gauge theories}, year = {2024}, month = {4/3/2024}, abstract = {

A key objective in nuclear and high-energy physics is to describe nonequilibrium dynamics of matter, e.g., in the early universe and in particle colliders, starting from the Standard Model. Classical-computing methods, via the framework of lattice gauge theory, have experienced limited success in this mission. Quantum simulation of lattice gauge theories holds promise for overcoming computational limitations. Because of local constraints (Gauss\&$\#$39;s laws), lattice gauge theories have an intricate Hilbert-space structure. This structure complicates the definition of thermodynamic properties of systems coupled to reservoirs during equilibrium and nonequilibrium processes. We show how to define thermodynamic quantities such as work and heat using strong-coupling thermodynamics, a framework that has recently burgeoned within the field of quantum thermodynamics. Our definitions suit instantaneous quenches, simple nonequilibrium processes undertaken in quantum simulators. To illustrate our framework, we compute the work and heat exchanged during a quench in a Z2 lattice gauge theory coupled to matter in 1+1 dimensions. The thermodynamic quantities, as functions of the quench parameter, evidence an expected phase transition. For general thermal states, we derive a simple relation between a quantum many-body system\&$\#$39;s entanglement Hamiltonian, measurable with quantum-information-processing tools, and the Hamiltonian of mean force, used to define strong-coupling thermodynamic quantities.

}, url = {https://arxiv.org/abs/2404.02965}, author = {Zohreh Davoudi and Christopher Jarzynski and Niklas Mueller and Greeshma Oruganti and Connor Powers and Nicole Yunger Halpern} } @article {2466, title = {Machine learning the thermodynamic arrow of time}, journal = {Nat. Phys.}, year = {2020}, month = {09/21/2020}, pages = {1-9}, abstract = {

The mechanism by which thermodynamics sets the direction of time\&$\#$39;s arrow has long fascinated scientists. Here, we show that a machine learning algorithm can learn to discern the direction of time\&$\#$39;s arrow when provided with a system\&$\#$39;s microscopic trajectory as input. The performance of our algorithm matches fundamental bounds predicted by nonequilibrium statistical mechanics. Examination of the algorithm\&$\#$39;s decision-making process reveals that it discovers the underlying thermodynamic mechanism and the relevant physical observables. Our results indicate that machine learning techniques can be used to study systems out of equilibrium, and ultimately to uncover physical principles.

}, doi = {https://doi.org/10.1038/s41567-020-1018-2}, url = {https://arxiv.org/abs/1909.12380}, author = {Alireza Seif and Mohammad Hafezi and Christopher Jarzynski} }