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Paper details
Number 4 - December 2020
Volume 30 - 2020
Basic quantum circuits for classification and approximation tasks
Joanna Wiśniewska, Marek Sawerwain, Andrzej Obuchowicz
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.
Keywords
quantum circuits, data classification, supervised learning, qubits, qudits