Quantum Machine Learning
521 papers for year 2024
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 169-180 of 521
Full classification of Pauli Lie algebras
Gerard Aguilar, Simon Cichy, Jens Eisert, Lennart Bittel
Generalization Error in Quantum Machine Learning in the Presence of Sampling Noise
Fangjun Hu, Xun Gao
Generating and analyzing small-size datasets to explore physical observables in quantum Ising systems
Rodrigo Carmo Terin
Geometric Quantum Machine Learning with Horizontal Quantum Gates
Roeland Wiersema, Alexander F. Kemper, Bojko N. Bakalov, Nathan Killoran
Graph Neural Networks on Quantum Computers
Yidong Liao, Xiao-Ming Zhang, Chris Ferrie
Hands-on Introduction to Randomized Benchmarking
Ana Silva, Eliska Greplova
Hardness of approximation for ground state problems
Sevag Gharibian, Carsten Hecht
Hardware-aware Circuit Cutting and Distributed Qubit Mapping for Connected Quantum Systems
Zefan Du, Yanni Li, Zijian Mo, Wenqi Wei, Juntao Chen, Rajkumar Buyya, Ying Mao
Harnessing Nth Root Gates for Energy Storage.
Fox EJ, Herrera M, Schmidt-Kaler F, D'Amico I.
Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States
Diksha Sharma, Vivek Balasaheb Sabale, Parvinder Singh, Atul Kumar
High-performance fault-tolerant quantum computing with many-hypercube codes.
Goto H.
How NOT to Fool the Masses When Giving Performance Results for Quantum Computers
Catherine McGeoch