Deep Ising Born Machine

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.