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 1633-1644 of 4,297
Validation of QuANTUM-derived tumor cell fraction for molecular testing in high-grade serous tubo-ovarian carcinoma.
Alviano AM, Pagni F, Seminati D, Casati G, Jaconi M, Fruscio R, De Leo A, Maloberti T, L'Imperio V, De Biase D, Cazzaniga G
Valorizing banana peel into carbon dots via pyrolysis: CCD optimization and machine learning prediction of fluorescent properties.
Nguyen DHH, Prokisch J
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara Abeysekara
Verifiable Sampling for Delegated Generative Quantum Machine Learning
Matteo Inajetovic, Anna Pappa, Petros Wallden
Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool
Ji Guan, Mingsheng Ying
Visual-to-Code Authoring, Tensor-Network Debugging, and Quantum-Circuit Inspection Tools in Python
Alejandro Mata Ali
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Anton Firc, Martin Perešíni, Vojtěch Mrázek, Kamil Malinka, Vojtěch Staněk, Zbyněk Lička, Nouhaila Innan, Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
Chi Zhang, Mengxin Zheng, Qian Lou, Hui Min Leung, Fan Chen
Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi, Frederic Cadet
Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust
Annie E. Paine, Smit Chaudhary, Antonio A. Gentile
What quantum computer to buy?
Alex Krasnok
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Bacui Li, Chandra Thapa, Tansu Alpcan, Udaya Parampalli