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 805-816 of 4,297
Machine learning the arrow of time in solid-state spins
Xiang-Qian Meng, Zhide Lu, Ya-Nan Lu, Xiu-Ying Chang, Yan-Qing Liu, Dong Yuan, Weikang Li, Zheng-Zhi Sun, Pei-Xin Shen, Lu-Ming Duan, Dong-Ling Deng, Pan-Yu Hou
Machine Learning the Strong Disorder Renormalization Group Method for Disordered Quantum Spin Chains
A. Ustyuzhanin, J. Vahedi, S. Kettemann
Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)
Vasily Bokov, Lisa Kohl, Sebastian Schmitt, Vedran Dunjko
Machine Learning-Accelerated Kinetic Simulations of Surface Reactions with Complex Coverage Effects.
Zhang Y, He J, Lai Z, Ling C, Li Q, Wang J
Machine learning-aided direct estimation of coherence and entanglement for unknown states
Ting Lin, Zhihua Chen, Kai Wu, Zhihua Guo, Zhihao Ma, Shao-Ming Fei
Machine learning-assisted carbon dot sensing for iron speciation in Auricularia auricula soaking solutions under various processing conditions.
Mao J, Li F, Ma Y, Qi L, Yuan M, Yuan X, Huang K
Machine learning-assisted high-speed combinatorial optimization with Ising machines for dynamically changing problems.
Hamakawa Y, Kashimata T, Yamasaki M, Tatsumura K
Machine Learning-Based Anomaly Detection in Post-Quantum TLS 1.3 Traffic: A Comparative Analysis on the CIC-PQC_OAV v1 Dataset
Serkan Keskin
Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre
Tom Lannon, Mauro Paternostro, John Martin Gregg, Amit Kumar
Machine Learning-Based Exploratory Performance Analysis of CRYSTALS-Kyber–AES Hybrid Post-Quantum Cryptographic Systems
Muhammed Shehu, Taiwo Kolajo, Emeka Ogbuju
Machine Learning-based Quantum Error Mitigation for Variational Algorithms
Nikita Korolev, Kirill Lakhmanskiy, Daniil Rabinovich
Machine learning-driven dual-objective optimization of biomass-derived carbon quantum dots toward high conductivity and yield.
Jiang H, Zhang H, Xu N, Hao Y, Zhang B, Su J, Li Y