Quantum Machine Learning
3,901 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 1753-1764 of 3,901
A Deep Learning Approach to Investigating Clandestine Laboratories Using a GC-QEPAS Sensor
Giorgio Felizzato, Nicola Liberatore, Sandro Mengali, Roberto Viola, Vittorio Moriggia, Francesco Saverio Romolo
A Fully Analog Pipeline for Portfolio Optimization
James S. Cummins, Natalia G. Berloff
A hybrid quantum solver for the Lorenz system
Sajad Fathi Hafshejani, Daya Gaur, Arundhati Dasgupta, Robert Benkoczi, Narasimha Gosala, Alfredo Iorio
A Hybrid Quantum-Classical Autoencoder Framework for End-to-End Communication Systems
Bolun Zhang, Gan Zheng, Nguyen Van Huynh
A Machine Learning Approach to Trapped Many-Fermion Systems
Paulo F. Bedaque, Hersh Kumar, Andy Sheng
A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets
Zhen-Xuan Fan, Sheng D. Chao
A Matrix Product State Model for Simultaneous Classification and Generation
Alex Mossi, Bojan Žunkovic, Kyriakos Flouris
A methodical approach to evaluate the potential of Quantum Computing for Manufacturing Simulations
Stefan Schröder, João Felipe, Sven Danz, Pascal Kienast, Alessandro Ciani, Philipp Ganser, Thomas Bergs
A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
Maida Wang, Jinyang Jiang, Peter V. Coveney
A physics-inspired evolutionary machine learning method: from the Schrödinger equation to an orbital-free-DFT kinetic energy functional
Juan I. Rodriguez, Ulises A. Vergara-Beltran
A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem
Deborah Volpe, Nils Quetschlich, Mariagrazia Graziano, Giovanna Turvani, Robert Wille
A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis.
Sidorov P, Tsuji N