Basic quantum circuits for classification and approximation tasks

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.

New directions for artificial intelligence: human, machine, biological, and quantum intelligence

“…The parallel learning mechanism of "once learning" has also been proposed for quantum computing (Weigang, 1998). Some scholars have made promising progress in this topic (Li F et al, 2004;Bhattacharyya et al, 2014;Konar et al, 2016;Wiśniewska et al, 2020).…”