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When computing the gradients of a quantum neural network using theparameter-shift rule, the cost function needs to be calculated twice for thegradient with respect to a single adjustable parameter of the network. When thetotal number of parameters is high, the quantum circuit for the computation hasto be adjusted and run for many times. Here we propose an approach to computeall the gradients using a single circuit only, with a much reduced circuitdepth and less classical registers. We also demonstrate experimentally, on bothreal quantum hardware and simulator, that our approach has the advantages thatthe circuit takes a significantly shorter time to compile than the conventionalapproach, resulting in a speedup on the total runtime.

Computing the gradients with respect to all parameters of a quantum neural network using a single circuit