can be comparable in magnitude to the energies that dictate the ligand-binding event. Another source of error common to quantum mechanics applications, such asmolecular orbitalcalculations, is the neglect ofsolvent effects(Tute and Kier, 1995). Yet despite errors of omission and approximation, ...
Introduction Quantum optimization is an emerging field hoping to solve optimization problems with the help of quantum algorithms running on quantum devices. What many scientists strive to find is, in fact, the evidence ofquantum advantage; that is, a quantum computer with a tailored quantum algorithm...
Quantum Generative Adversarial Networks (QGANs) represent a useful development in quantum machine learning, using the particular properties of quantum mechanics to solve the challenges of data analysis and modeling. This paper brings up a general analysis of five QGAN architectures, focusing on their ...
A general-purpose quantum computer can indeed efficiently simulate quantum mechanics in this sense for many physically realistic cases, such as systems with locality restrictions on their interactions.44Given a description of a quantum state|ψ〉, a description ofH, and a timet, the quantum simulat...
One of the goals of quantum computation and quantum information is to develop tools which sharpen our intuition about quantum mechanics, and make its predictions more transparent to human minds. (查看原文) spinova2 回复2012-05-17 23:56:00 ...
Quantum computing is an emergent field of cutting-edge computer science harnessing the unique qualities of quantum mechanics to solve problems beyond the ability of even the most powerful classical computers. The field of quantum computing contains a range of disciplines, including quantum hardware and...
Providing additive-error approximation algorithms for various matrix functions beating best-known classical algorithms, such as the hafnian of a complex symmetric matrix (Theorem 1) and the permanent of HPSD matrix (Theorem 2) Providing multiplicative-error approximation algorithms for various matrix func...
The involved quantum dynamic optimization algorithms are proven to have better performance. However, these algorithms only use the theory of quantum mechanics and are still executed in classical computers. Quantum computers developed by research entities such as IBM, Intel, and Google are progressing, ...
Liu, J., Mao, L., Zhang, P. & Wang, L. Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits.Mach. Learn. Sci. Technol.2, 025011 (2021). ArticleGoogle Scholar Sim, S., Johnson, P. D. & Aspuru-Guzik, A. Expressibility and entangling capabilit...
Since Feynman’s proposal to compute using quantum mechanics3, many potential applications have emerged, including factoring4, optimization5, machine learning6, quantum simulation7and quantum chemistry8. These applications often require billions of quantum operations9,10,11and state-of-the-art quantum ...