Quantum circuit design using neural networks assisted by entanglement

Abstract

This work introduces a novel structure based on universal quantum circuits which uses classical machine learning techniques in order to solve a quantum problem: the decomposition of a generic quantum operator into a sequence of elementary unitary matrices (universal basic quantum gates). Even though the postulates of quantum mechanics guarantee any unitary operation as a feasible operation over a quantum system, there is no simple method to implement an arbitrary algorithm. By means of a multilayer hybrid neural network in which the basic cell is made up of CNOTs and universal one-qubit unitary gates, this work offers a solution to the given problem. These specific gates were chosen since they are the gates available in real quantum computers such as IBMQ's quantum processors. The network learns the unitary gates classically using the method of the steepest descent and is aided in learning the entangling gates by the use of two types of quantum correlations: Mutual Information (MI) and Cumulative Correlation Measure (CCM). The algorithm implemented in this case is a type of supervised learning. The results show that the model fits the data gracefully and correctly predicts a wide range of algorithms.