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Experimental Quantum End-to-End Learning on a Superconducting Processor
[article]
2022
arXiv
pre-print
Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully
arXiv:2203.09080v1
fatcat:fxm2crioajd6fehjt2wqug3l5a