Quantum Machine Learning
3,957 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 2089-2100 of 3,957
Pilot-Quantum: A Quantum-HPC Middleware for Resource, Workload and Task Management
Pradeep Mantha, Florian J. Kiwit, Nishant Saurabh, Shantenu Jha, Andre Luckow
PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms
Kamila Zaman, Alberto Marchisio, Muhammad Kashif, Muhammad Shafique
Post-Quantum Cryptography (PQC) Network Instrument: Measuring PQC Adoption Rates and Identifying Migration Pathways
Jakub Sowa, Bach Hoang, Advaith Yeluru, Steven Qie, Anita Nikolich, Ravishankar Iyer, Phuong Cao
Post-Quantum Cryptography: Securing AI Systems against Quantum Threats
Danny Smith, Akinniyi James Samuel
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt Dubhashi, Alexandru Gheorghiu
Predicting properties of quantum systems by regression on a quantum computer
Andrey Kardashin, Yerassyl Balkybek, Vladimir V. Palyulin, Konstantin Antipin
Predicting quantum learnability from landscape fluctuation
Hao-Kai Zhang, Chenghong Zhu, Xin Wang
Probing many-body Bell correlation depth with superconducting qubits
Ke Wang, Weikang Li, Shibo Xu, Mengyao Hu, Jiachen Chen, Yaozu Wu, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Xu Zhang, Pengfei Zhang, Wenjie Jiang, Zhide Lu, Zheng-Zhi Sun, Hekang Li, Qiujiang Guo, Zhen Wang, Patrick Emonts, Jordi Tura, Chao Song, H. Wang, Dong-Ling Deng
Proposal for Superconducting Quantum Networks Using Multi-Octave Transduction to Lower Frequencies
Takuma Makihara, Wentao Jiang, Amir H. Safavi-Naeini
Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization
Veronica Panizza, Philipp Hauke, Cristian Micheletti, Pietro Faccioli
Provable bounds for noise-free expectation values computed from noisy samples.
Barron SV, Egger DJ, Pelofske E, Bärtschi A, Eidenbenz S, Lehmkuehler M, Woerner S.
Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics
Changnan Peng, Jin-Peng Liu, Gia-Wei Chern, Di Luo