<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Basic quantum circuits for classification and approximation tasks

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Basic quantum circuits for classification and approximation tasks"}

We discuss a quantum circuit construction designed for classification. The circuit is built of regularly placed elementary quantum gates, which implies the...

Basic quantum circuits for classification and approximation tasks

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 733 - 744

Eingereicht: 23. Jan. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.34768/amcs-2020-0054

Schlüsselwörter
quantum circuits, data classification, supervised learning, qubits, qudits

© 2020 Joanna Wiśniewska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Basic quantum circuits for classification and approximation tasks

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 733 - 744

Eingereicht: 23. Jan. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.34768/amcs-2020-0054

Schlüsselwörterquantum circuits, data classification, supervised learning, qubits, qudits

© 2020 Joanna Wiśniewska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
quantum circuits, data classification, supervised learning, qubits, qudits