Quantum Machine Learning
416 papers for year 2023
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 109-120 of 416
Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning
Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal, Amartya S. Banerjee, Susanta Ghosh
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Shuxiang Cao, Weixi Zhang, Jules Tilly, Abhishek Agarwal, Mustafa Bakr, Giulio Campanaro, Simone D Fasciati, James Wills, Boris Shteynas, Vivek Chidambaram, Peter Leek, Ivan Rungger
Engineered dissipation to mitigate barren plateaus
Antonio Sannia, Francesco Tacchino, Ivano Tavernelli, Gian Luca Giorgi, Roberta Zambrini
Enhancing collective entanglement witnesses through correlation with state purity
Kateřina Jiráková, Antonín Černoch, Artur Barasiński, Karel Lemr
Enhancing the performance of an open quantum battery by adjusting its velocity
Mojaveri B, Bahrbeig RJ, Fasihi MA, Babanzadeh S.
Enhancing the Security of the BB84 Quantum Key Distribution Protocol against Detector-Blinding Attacks via the Use of an Active Quantum Entropy Source in the Receiving Station.
Stipčević M.
Entanglement transition in deep neural quantum states
Giacomo Passetti, Dante M. Kennes
Entanglement transitions induced by quantum-data collection
Shane P. Kelly, Jamir Marino
Entanglement Verification with Deep Semi-supervised Machine Learning
Lifeng Zhang, Zhihua Chen, Shao-Ming Fei
EntangleVR++: evaluating the potential of using entanglement in an interactive VR scene creation system
Mengyu Chen, Marko Peljhan, Misha Sra, Misha Sra
Evaluating quantum generative models via imbalanced data classification benchmarks
Graham R. Enos, Matthew J. Reagor, Eric Hulburd
Evaluating the Convergence Limit of Quantum Neural Tangent Kernel
Trong Duong