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With the constant increase of the number of quantum bits (qubits) in theactual quantum computers, implementing and accelerating the prevalent deeplearning on quantum computers are becoming possible. Along with this trend,there emerge quantum neural architectures based on different designs of quantumneurons. A fundamental question in quantum deep learning arises: what is thebest quantum neural architecture? Inspired by the design of neuralarchitectures for classical computing which typically employs multiple types ofneurons, this paper makes the very first attempt to mix quantum neuron designsto build quantum neural architectures. We observe that the existing quantumneuron designs may be quite different but complementary, such as neurons fromvariational quantum circuits (VQC) and Quantumflow. More specifically, VQC canapply real-valued weights but suffer from being extended to multiple layers,while QuantumFlow can build a multi-layer network efficiently, but is limitedto use binary weights. To take their respective advantages, we propose to mixthem together and figure out a way to connect them seamlessly withoutadditional costly measurement. We further investigate the design principles tomix quantum neurons, which can provide guidance for quantum neural architectureexploration in the future. Experimental results demonstrate that the identifiedquantum neural architectures with mixed quantum neurons can achieve 90.62% ofaccuracy on the MNIST dataset, compared with 52.77% and 69.92% on the VQC andQuantumFlow, respectively.

Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs