Thermalizing quantum machines: dissipation and entanglement.

Phys Rev Lett

Group of Applied Physics, University of Geneva, 20, rue de l'Ecole-de-Médecine, CH-1211 Geneva 4, Switzerland.

Published: March 2002

We study the relaxation of a quantum system towards the thermal equilibrium using tools developed within the context of quantum information theory. We consider a model in which the system is a qubit, and reaches equilibrium after several successive two-qubit interactions (thermalizing machines) with qubits of a reservoir. We characterize completely the family of thermalizing machines. The model shows a tight link between dissipation, fluctuations, and the maximal entanglement that can be generated by the machines. The interplay of quantum and classical information processes that give rise to practical irreversibility is discussed.

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevLett.88.097905DOI Listing

Publication Analysis

Top Keywords

thermalizing machines
8
thermalizing quantum
4
machines
4
quantum machines
4
machines dissipation
4
dissipation entanglement
4
entanglement study
4
study relaxation
4
relaxation quantum
4
quantum system
4

Similar Publications

Mulberry silk (Bombyx mori) and eri silk (Samia/Philosamia ricini) are widely used silks. Eri silk is a wild silk that contains an arginine-glycine-aspartic acid tripeptide sequence within its structure, making it a potential and sustainable biomaterial. However, its poor solubility using conventional methods has resulted in limited research compared with that of mulberry silk fibroin.

View Article and Find Full Text PDF

Purpose: To investigate how varying ferrule heights and the number of glass fiber posts affect fracture resistance and behavior of endodontically treated maxillary first premolars with substantial loss of tooth structure.

Materials And Methods: Twenty-four extracted endodontically treated human maxillary first premolars were divided into three groups (n = 8) based on ferrule height and post number. The groups were as follows: premolars of 2 mm ferrule height that were restored with single posts (control group), premolars of 0.

View Article and Find Full Text PDF

Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically.

View Article and Find Full Text PDF

Diffusion, mechanical and thermal properties of sT hydrogen hydrate by machine learning potential.

J Phys Condens Matter

January 2025

Physics, Xiamen University, Wulijidian Building 358, Haiyun campus, Xiamen University, Xiamen, Fujian, 361005, CHINA.

Newly-synthesized structure T (sT) hydrate show promising practical applications in hydrogen storage and transport, yet the properties remain poorly understood. Here, we develop a machine learning potential (MLP) of sT hydrogen hydrate derived from quantum-mechanical molecular dynamics (MD) simulations. Using this MLP forcefield, the structural, hydrogen diffusion, mechanical and thermal properties of sT hydrogen hydrate are extensively explored.

View Article and Find Full Text PDF

Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural environments. Biodegradable polymers, while promising for reducing environmental impact, account for less than 2% of total polymer production. To expand the availability of biodegradable polymers, research has explored structure-biodegradability relationships, yet most studies focus on specific polymers, necessitating further exploration across diverse polymers.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!