Quantum Machine Learning
4,297 papers
Quantum Machine Learning Research Context
This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.
Showing 925-936 of 4,297
Off-line quantum-advantage feature extraction for industrial production
Carlos Flores-Garrigos, Gabriel D. Alvarado Barrios, Qi Zhang, Anton Simen, Enrique Solano
On Best-Possible One-Time Programs
Aparna Gupte, Jiahui Liu, Luowen Qian, Justin Raizes, Bhaskar Roberts, Mark Zhandry
On linearity or non-linearity in machine learning for quantum chaotic dynamics
Francesco Perciavalle, Agostino Gallo, Francesco Plastina, Gianluigi Greco, Nicola Lo Gullo, Carlo Adornetto
On measurement-dependent variance in quantum neural networks
Andrey Kardashin, Konstantin Antipin
On Quantum Learning Advantage Under Symmetries
Tuyen Nguyen, Mária Kieferová, Amira Abbas
On the average-case complexity of learning states from the circular and Gaussian ensembles
Maxwell West
On the Computational Reproducibility of Human-Computer Interaction
Olga Iarygina, Kasper Anders Søren Hornbæk, Aske ; id_orcid 0000-0003-1827-8513 Mottelson
On the fundamental resource for exponential advantage in quantum channel learning.
Kim M, Oh C.
On the importance of hyperparameters in initializing parameterized quantum circuits
Ankit Kulshrestha, Sarvagya Upadhyay
On The Multi-target Security of Post-Quantum Key Encapsulation Mechanisms
Lewis Glabush, Kathrin Hövelmanns, Douglas Stebila
On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework
Dario Maio
One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms
Sayantan Pramanik, M Girish Chandra