Journal
PHYSICAL REVIEW A
Volume 102, Issue 5, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.102.052421
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Funding
- National Key R&D Program of China [2018YFA0703800]
- National Natural Science Foundation of China [61873317, 61873262, 61733018]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515011375]
- Youth Innovation Promotion Association of the CAS
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Quantum computing enables quantum neural networks (QNNs) to have great potential to surpass artificial neural networks. The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are polynomial in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.
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