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...
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,...
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...
Out-of-distribution generalization for learning quantum dynamics 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 trai.....
Covariant quantum kernels for data with group structure Article 19 January 2024 Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks Article Open access 10 January 2025 Introduction Learning machines aim to find statistical patterns in data ...
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 ...
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,EA Power,T Thirun...
Complete QIM is of great significance in quantum foundation ... Zhi-Feng Liu,Wei-Min Shang,Jia-Min Xu,... - Communications Physics 被引量: 0发表: 2025年 Research and Improvement of Classification Methods for Multi-class Support Vector Machines The excellences and defections of the existing ...
In such a case, a common key is derived based on associativity. 3.5 Schemes based on lattices Due to strong security guarantees, lattice based schemes have become a strong alternative for post-quantum cryptography. Since the seminal work of Regev [68] on the learning with errors problem (LWE...
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...