Basic quantum circuits for classification and approximation tasks

International Journal of Applied Mathematics and Computer Science

Obuchowicz Andrzej¹  Sawerwain Marek¹  Wiśniewska Joanna² 

[1] Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516Zielona Góra, Poland;Institute of Information Systems, Military University of Technology, ul. Gen. S. Kaliskiego 2, 00-908Warsaw, Poland;

关键词: quantum circuits;    data classification;    supervised learning;    qubits;    qudits;   

DOI  :  10.34768/amcs-2020-0054

来源: DOAJ

【 摘 要 】

We discuss a quantum circuit construction designed for classification. The circuit is built of regularly placed elementary quantum gates, which implies the simplicity of the presented solution. The realization of the classification task is possible after the procedure of supervised learning which constitutes parameter optimization of Pauli gates. The process of learning can be performed by a physical quantum machine but also by simulation of quantum computation on a classical computer. The parameters of Pauli gates are selected by calculating changes in the gradient for different sets of these parameters. The proposed solution was successfully tested in binary classification and estimation of basic non-linear function values, e.g., the sine, the cosine, and the tangent. In both the cases, the circuit construction uses one or more identical unitary operations, and contains only two qubits and three quantum gates. This simplicity is a great advantage because it enables the practical implementation on quantum machines easily accessible in the nearest future.

【 授权许可】

Unknown