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Quantum Thermodynamics of Nonequilibrium Processes in Lattice Gauge Theories.
Phys Rev Lett
December 2024
Joint Center for Quantum Information and Computer Science, NIST and University of Maryland, College Park, Maryland 20742, USA.
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 of particle physics.
View Article and Find Full Text PDFIR-Driven Multisignal Conditioning for Multiplex Detection: Thermal-Responsive Triple DNA-Mediated Reconfigurable Photoelectrochemical/Photothermal Dual-Mode Strategy.
ACS Sens
January 2025
College of Chemistry, Jilin Province Research Center for Engineering and Technology of Spectral Analytical Instruments, Jilin University, Changchun 130012, China.
Superior to traditional multiplex photoelectrochemical (PEC) sensors, integrated multitarget assay on a single reconstructive electrode interface is promising in real-time detection through eliminating the need of specialized instrumentation and cumbersome interfacial modifications. Current interface reconstruction approaches including pH modulation and bioenzyme cleavage involve biohazardous and time-consuming operations, which cannot meet the demand for rapid, eco-friendly, and portable detection, which are detrimental to the development of multiplex PEC sensors toward portability. Herein, we report a pioneer work on IR-driven "four-to-one" multisignal conditioning to facile reconfigure electrode interface for multitarget detection via photoelectrochemical/photothermal dual mode.
View Article and Find Full Text PDFPM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method.
J Chem Theory Comput
January 2025
Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 160 00 Prague, Czech Republic.
Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction.
View Article and Find Full Text PDFReinforcement Learning Optimization of the Charging of a Dicke Quantum Battery.
Phys Rev Lett
December 2024
Freie Universität Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany.
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Because of its experimental feasibility, the Dicke battery-which comprises N two-level systems coupled to a common photon mode-is one of the most promising designs for quantum batteries. However, the chaotic nature of the model severely hinders the extractable energy (ergotropy).
View Article and Find Full Text PDFEnergy-Filtered Quantum States and the Emergence of Nonlocal Correlations.
Phys Rev Lett
December 2024
Université Paris-Saclay, CNRS, LPTMS, 91405, Orsay, France.
Energy-filtered quantum states are promising candidates for efficiently simulating thermal states. We explore a protocol designed to transition a product state into an eigenstate located in the middle of the spectrum; this is achieved by gradually reducing its energy variance, which allows us to comprehensively understand the crossover phenomenon and the subsequent convergence toward thermal behavior. We introduce and discuss three energy-filtering regimes (short, medium, and long), and we interpret them as stages of thermalization.
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