Abstract:Quantum Neural Network (QNN) is drawing increasing research interest thanks to its potential to achieve quantum advantage on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable QNN learning, the training process needs to be offloaded to real quantum machines instead of using exponentialcost classical simulators. One common approach to obtain QNN gradients is parameter shift whose cost scales linearly with the number of qubits. We present On-chip QNN, the first experimental … Show more
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