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 385-396 of 416
Thermodynamic Linear Algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon, Samuel Duffield, Thomas Ahle, Daniel Simpson, Gavin E. Crooks, Patrick J. Coles
Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
Alistair Letcher, Stefan Woerner, Christa Zoufal
Toward a Unified Hybrid HPCQC Toolchain
Philipp Seitz, Amr Elsharkawy, Xiao-Ting Michelle To, Martin Schulz
Toward Automated Quantum Variational Machine Learning
Omer Subasi
Toward Consistent High-fidelity Quantum Learning on Unstable Devices via Efficient In-situ Calibration
Zhirui Hu, Robert Wolle, Mingzhen Tian, Qiang Guan, Travis Humble, Weiwen Jiang
Toward Privacy in Quantum Program Execution On Untrusted Quantum Cloud Computing Machines for Business-sensitive Quantum Needs
Tirthak Patel, Daniel Silver, Aditya Ranjan, Harshitta Gandhi, William Cutler, Devesh Tiwari
Towards a dissipative quantum classifier
He Wang, Chuanbo Liu, Jin Wang
Towards an in-depth detection of malware using distributed QCNN
Tony Quertier, Grégoire Barrué
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller, Danielle Schuman, Claudia Linnhoff-Popien
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
Léo Monbroussou, Eliott Z. Mamon, Jonas Landman, Alex B. Grilo, Romain Kukla, Elham Kashefi
Transfer learning from Hermitian to non-Hermitian quantum many-body physics
Sharareh Sayyad, Jose L. Lado
Trustworthy Multi-Modal AI in Healthcare: A Comprehensive Framework for Bias Detection, Explanation, and Mitigation
Daniel Anderson