The conceptual analysis of quantum mechanics brings to light that a theory inherently consistent with observations should be able to describe both quantum and classical systems, i.e., quantum-classical hybrids. For example, the orthodox interpretation of measurements requires the transient creation of quantum-classical hybrids. Despite its limitations in defining the classical limit, Ehrenfest's theorem makes the simplest contact between quantum and classical mechanics. Here, we generalized the Ehrenfest theorem to bipartite quantum systems. To study quantum-classical hybrids, we employed a formalism based on operator-valued Wigner functions and quantum-classical brackets. We used this approach to derive the form of the Ehrenfest theorem for quantum-classical hybrids. We found that the time variation of the average energy of each component of the bipartite system is equal to the average of the symmetrized quantum dissipated power in both the quantum and the quantum-classical case. We expect that these theoretical results will be useful both to analyze quantum-classical hybrids and to develop self-consistent numerical algorithms for Ehrenfest-type simulations.
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http://dx.doi.org/10.3390/e25040602 | DOI Listing |
Sci Rep
January 2025
Purwanchal Campus Institute of Engineering, Tribhuvan University, Kirtipur, Nepal.
Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel quantum machine learning framework for stabilizing computation offloading in next-generation MEC systems. Our approach leverages hybrid quantum-classical neural networks to learn optimal offloading policies that maximize network performance while ensuring the stability of data queues, even under dynamic and unpredictable network conditions.
View Article and Find Full Text PDFNPJ Comput Mater
December 2024
Department of Chemistry, University of Zurich, Winterthurerstrasse 190, Zurich, 8057 Switzerland.
We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding approaches based on an orbital space separation of the fragment and environment degrees of freedom. We demonstrate its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the variational quantum eigensolver and the quantum equation-of-motion algorithm are used to obtain the low-lying spectrum of the embedded fragment Hamiltonian. The application of this scheme to study localized electronic states in materials is showcased through the accurate prediction of the optical properties of the neutral oxygen vacancy in magnesium oxide (MgO).
View Article and Find Full Text PDFChem Sci
January 2025
Faculty of Chemistry, Institute of Theoretical Chemistry, Universität Wien A-1090 Vienna Austria
Recent developments in quantum computing are highly promising, particularly in the realm of quantum chemistry. Due to the noisy nature of currently available quantum hardware, hybrid quantum-classical algorithms have emerged as a reliable option for near-term simulations. Mixed quantum-classical dynamics methods effectively capture nonadiabatic effects by integrating classical nuclear dynamics with quantum chemical computations of the electronic properties.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.
Photosystem II (PSII) catalyzes light-driven water oxidation that releases dioxygen into our atmosphere and provides the electrons needed for the synthesis of biomass. The catalysis occurs in the oxygen-evolving oxo-manganese-calcium (MnOCa) cluster that drives the oxidation and deprotonation of substrate water molecules leading to the O formation. However, despite recent advances, the mechanism of these reactions remains unclear and much debated.
View Article and Find Full Text PDFSci Adv
December 2024
Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA.
De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics-a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing.
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