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Short-time accuracy and intra-electron correlation for nonadiabatic quantum-classical mapping approaches.
J Chem Phys
December 2024
Pitaevskii BEC Center, CNR-INO and Dipartimento di Fisica, Università di Trento, Via Sommarive 14, Trento I-38123, Italy.
Nonadiabatic quantum-classical mapping approaches have significantly gained in popularity over the past several decades because they have acceptable accuracy while remaining numerically tractable even for large system sizes. In the recent few years, several novel mapping approaches have been developed that display higher accuracy than the traditional Ehrenfest method, linearized semiclassical initial value representation (LSC-IVR), and Poisson bracket mapping equation (PBME) approaches. While various benchmarks have already demonstrated the advantages and limitations of those methods, unified theoretical justifications of their short-time accuracy are still demanded.
View Article and Find Full Text PDFQuantum classical hybrid convolutional neural networks for breast cancer diagnosis.
Sci Rep
October 2024
College of Computer Science And Cyber Security(Pilot Software College), Chengdu University of Technology, Chengdu, 610059, China.
The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy.
View Article and Find Full Text PDFPower flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach.
Sci Rep
October 2024
Electrical Sustainable Energy, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands.
Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others.
View Article and Find Full Text PDFA hybrid quantum-classical classification model based on branching multi-scale entanglement renormalization ansatz.
Sci Rep
August 2024
College of Information Science and Engineering, ZaoZhuang University, Zaozhuang, 277160, China.
Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper introduces a hybrid quantum-classical classification model based on BMERA and explores the correlation between circuit layout, expressiveness, and classification accuracy.
View Article and Find Full Text PDFHow Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?
Nanomaterials (Basel)
July 2024
National Energy Technology Laboratory, U. S. Department of Energy, Pittsburgh, PA 15236, USA.
Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits.
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