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 361-372 of 416
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Hao-kai Zhang, Chenghong Zhu, Mingrui Jing, Xin Wang
Statistical Complexity of Quantum Learning
Leonardo Banchi, Jason Luke Pereira, Sharu Theresa Jose, Osvaldo Simeone
Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing
Suryansh Upadhyay, Swaroop Ghosh
Study of quantum algorithms and their implementations
Giancarlo P. Gamberi, Calebe P. Bianchini
SU(d)-Symmetric Random Unitaries: Quantum Scrambling, Error Correction, and Machine Learning
Zimu Li, Han Zheng, Yunfei Wang, Liang Jiang, Zi-Wen Liu, Junyu Liu
Sub-universal variational circuits for combinatorial optimization problems
Gal Weitz, Lirandë Pira, Chris Ferrie, Joshua Combes
Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks
Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan, Marti Vives, Esin Türeci, Leon Bello, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci
TECHNOLOGICAL INNOVATIONS IN EDUCATION: STUDYING THE INTERNET OF THINGS, BLOCKCHAIN AND QUANTUM COMPUTING FOR MODERN LEARNING
И.Р. Рустамбеков
Tensor Network Based Efficient Quantum Data Loading of Images
Jason Iaconis, Sonika Johri
Tensor Networks for Explainable Machine Learning in Cybersecurity
Borja Aizpurua, Samuel Palmer, Roman Orus
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal, Bijaya K. Panigrahi
Testing of Hybrid Quantum-Classical K-Means for Nonlinear Noise Mitigation
Ark Modi, Alonso Viladomat Jasso, Roberto Ferrara, Christian Deppe, Janis Noetzel, Fred Fung, Maximilian Schaedler