Journal
NPJ QUANTUM INFORMATION
Volume 9, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41534-023-00685-w
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Quantum computer can boost machine learning through its inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, an end-to-end learning model design approach was proposed, where the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Experimental realization of quantum end-to-end machine learning on a superconducting processor is reported. The trained model achieved 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST database, demonstrating great potential for resolving complex real-world tasks when more qubits are available.
Machine learning can be enhanced by a quantum computer via its 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 exploit limited quantum resources. Here, we report the experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available.
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