Affiliation:
1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai 200237 China
Abstract
AbstractA quantum neural network (QNN) is a method to find patterns in quantum data and has a wide range of applications including quantum chemistry, quantum computation, quantum metrology, and quantum simulation. Efficiency and universality are two desirable properties of a QNN but are unfortunately contradictory. In this work, a deep Ising Born machine (DIBoM) is examined, and shown that it has a good balance between efficiency and universality. More precisely, the DIBoM has a flexible number of parameters to be efficient, and achieves provable universality with sufficient parameters. The architecture of the DIBoM is based on generalized controlled‐Z gates, conditional gates, and some other ingredients. To compare the universality of the DIBoM with other QNNs, a fidelity‐based expressivity measure is proposed, which may be of independent interest. Extensive empirical evaluations corroborate that the DIBoM is both efficient and expressive.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
East China University of Science and Technology
Subject
Electrical and Electronic Engineering,Computational Theory and Mathematics,Condensed Matter Physics,Mathematical Physics,Nuclear and High Energy Physics,Electronic, Optical and Magnetic Materials,Statistical and Nonlinear Physics