Born machine model based on matrix product state quantum circuit

Born machine model based on the probability interpretation of the wave function combining quantum information theory with machine learning method provides a new tool to study the generative models. The Born machine model with a general parameterized quantum circuit generally requires the same number of qubits as the sample feature size of the dataset to be processed, while each sample usually contains thousands of features in actual dataset. A novel Born machine model with a matrix product state quantum circuit is proposed, which requires less qubits than that with a general parameterized quantum circuit, so it can make better use of scarce qubit resources in near-term quantum devices. And the presented Born machine model is trained with the maximal mean discrepancy loss function. The learning process of the proposed Born machine model is numerically simulated on the Bars-and-Stripes dataset. The simulation results verify the feasibility of the Born machine model with the matrix product state quantum circuit. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

The Born machine model based on the probability interpretation of the wave function, combined with quantum information theory and machine learning methods, is a new tool for studying generative models. A novel Born machine model with a matrix product state quantum circuit is proposed, which allows for more efficient utilization of qubit resources.