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...
The Variational Monte Carlo (VMC) is a promising approach for computing the ground state energy of many-body quantum problems and attracts more and more in... TC Li,F Chen,H Chen,... 被引量: 0发表: 2023年 Provable Stochastic Algorithm for Large-Scale Fully-Connected Tensor Network Decompos...
Spin generalization of the relativistic Calogero - Sutherland model is constructed by using the affine Hecke algebra and shown to possess the quantum affin... H Konno - 《Journal of Physics A Mathematical & General》 被引量: 19发表: 1996年 加载更多研究...
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...
14 国际基础科学大会-Analyticity in spin and causality constraints-Simon Caron Huot 53:05 国际基础科学大会-The Science of Artificial Intelligence-Yi Zeng 59:01 国际基础科学大会-Recent Advances in Learning Quantum Phenomena-Jerry Zheng Li 55:44 国际基础科学大会-Sparse Fourier Transform in the ...
Universal generalization is the rule of inference that allows us to conclude that ∀xP(x) is true, given the premise that P(a) is true for all elements a in the domain. From: Discrete Mathematics, 2023 About this pageSet alert
Covariant quantum kernels for data with group structure Article 19 January 2024 Piecewise linear neural networks and deep learning Article 09 June 2022 Introduction Learning machines aim to find statistical patterns in data that generalize to previously unseen samples1. How well they perform in doing...
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 Exploiting Task Relatedness for Multiple Task Learning Summary: The...
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution....
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 ...