Work

Deep Ising Born Machine

Year: 2023

Type: article

Abstract: A 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.

Source: Advanced Quantum Technologies

DOI: 10.1002/qute.202300033

Author: Zhu Cao

Institutions: East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Natural Science Foundation of Shanghai, National Natural Science Foundation of China, East China University of Science and Technology

Awards: G150277798, 高效通用量子神经网络的构建与分析, G5789780205, G6940177217, G791386250