Quantum machine learning (QML) is an emerging field that has generated great excitement6,7,8,9. Modern QML typically involves training a parameterized quantum circuit in order to analyze either classical or quantum data sets10,11,12,13,14,15,16. Early results indicate that, for classical data...
Quantum computing for realistic problems Quantum machine learning Applying machine learning in quantum computing Enhancing machine learning leveraging quantum computing Quantum experiment Efficient benchmarking and calibration of quantum hardware Expe...
Peter Wittek, in Quantum Machine Learning, 2014 7.6 Generalization Performance To guarantee good generalization performance, we expect a low VC dimension to give a tight bound on the expected error (Section 2.5). Oddly, support vector machines may have a high or even infinite VC dimension. Yet,...
This quantum advantage in the decision-making process of the quantum PS agent was recently experimentally demonstrated using a small-scale quantum information processor based on trapped ions43. In the PS model, learning is realized by internal modification of the clip network, both in terms of its...
Each of the individual interactions is context-independently law-like, determined by the quantum mechanics of electron exchange interactions. But each such reaction occurs in the spatially organised context of all the others and only in that complex empirical constraint context do they constitute ...
Bayesian Quantum Neural Network for Renewable-Rich Power Flow with Training Efficiency and Generalization Capability Improvements 来自 arXiv.org 喜欢 0 阅读量: 1 作者:Zhu, Ziqing,Zhu, Shuyang,Bu, Siqi 摘要: This paper addresses the challenges of power flow calculation in large scale power systems ...
On a Generalization of the Power-Zienau-Woolley Transformation in Quantum Electrodynamics and Atomic Field Equations A canonical transformation is performed on the conventional Hamiltonian for the electromagnetic radiation field and an assemblage of neutral molecules in i... M.,Babiker,E.,... - 《...
We show that quantum systems of extended objects naturally give rise to a large class of exotic phases - namely topological phases. These phases occur when... MA Levin,XG Wen - 《Physical Review B》 被引量: 630发表: 2004年 Construction of fermionic string models in four dimensions The const...
RS Sutton - 《Machine Learning Proceedings》 被引量: 225发表: 1995年 Gauge Field Theory Coherent States (GCS) : IV. Infinite Tensor Product and Thermodynamical Limit In the canonical approach to Lorentzian Quantum General Relativity in four spacetime dimensions an important step forward has been mad...
Jo, Y. et al. Quantitative phase imaging and artificial intelligence: a review.IEEE J. Sel. Top. Quantum Electron.25, 6800914 (2019). ArticleGoogle Scholar Wang, K. Q. et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction.Opt. Lett.44, 4765–4768 ...