Results 141 to 150 of about 8,042,432 (193)

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms. [PDF]

Entropy (Basel), 2022
Mancilla J, Pere C.
europepmc   +1 more source

A systematic review of quantum machine learning for digital health. [PDF]

NPJ Digit Med
Gupta RS, Wood CE, Engstrom T, Pole JD, Shrapnel S.   +4 more
europepmc   +1 more source

Quantum machine learning driven optimization of nutrient-hormone interactions for enhanced in vitro regeneration of common bean. [PDF]

BMC Plant Biol
Katırcı R, Aasim M, Aadil F, Chohan RT.   +3 more
europepmc   +1 more source

Power of data in quantum machine learning. [PDF]

Nat Commun, 2021
Huang HY, Broughton M, Mohseni M, Babbush R, Boixo S, Neven H, McClean JR.   +6 more
europepmc   +1 more source

A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data. [PDF]

Sci Rep
Priyadharshini M, Raju BD, Banu AF, Kumar PJ, Murugesh V, Rybin O.   +5 more
europepmc   +1 more source

Information Scrambling in quantum extreme learning machines for quantum state tomography

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models.
Marco Vetrano
core  

QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution. [PDF]

Brief Bioinform
Matsubara T, Machida S, Owusu SPK, Asakura A, Hashimoto H, Matsuoka M, Nagasaki M.   +6 more
europepmc   +1 more source

Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements. [PDF]

MethodsX
Devadas RM, T S.
europepmc   +1 more source

Heterophily-informed Message Passing

Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Graph neural networks (GNNs) are known to be vulnerable to oversmoothing due to their implicit homophily assumption.
Solin, Arno, Garg, Vikas, Wang, Haishan
core  

Quantum machine learning with Adaptive Boson Sampling via post-selection. [PDF]

Nat Commun
Hoch F, Caruccio E, Rodari G, Francalanci T, Suprano A, Giordani T, Carvacho G, Spagnolo N, Koudia S, Proietti M, Liorni C, Cerocchi F, Albiero R, Di Giano N, Gardina M, Ceccarelli F, Corrielli G, Chabaud U, Osellame R, Dispenza M, Sciarrino F.   +20 more
europepmc   +1 more source