Quantum-machine-learning channel discrimination

TL;DR

This paper explores the use of variational quantum circuits and machine learning techniques for quantum channel discrimination, demonstrating their effectiveness in distinguishing noise channels with fewer resources and improved accuracy.

Contribution

It introduces practical variational quantum algorithms for channel discrimination, compares strategies, and enhances kernel methods, advancing quantum machine learning applications in noise identification.

Findings

Sequential strategy outperforms parallel in convergence and resource use.

Quantum classifiers work with mixed and random input states.

Kernel modifications improve discrimination efficiency.

Abstract

In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of variational quantum circuits and machine learning techniques for discriminating such channels. In particular, we explore (i) the practical implementation of embedding this task into the framework of variational quantum computing, (ii) training a quantum classifier based on variational quantum circuits, and (iii) applying the quantum kernel estimation technique. For testing these three channel discrimination approaches, we considered a pair of entanglement-breaking channels and the depolarizing channel with two different depolarization factors. For the approach (i), we address solving the quantum channel discrimination problem using widely discussed parallel and sequential strategies. We show the advantage of the latter in terms of better convergence with less quantum resources. Quantum channel discrimination with a variational quantum classifier (ii) allows one to operate even with random and mixed input states and simple variational circuits. The kernel-based classification approach (iii) is also found effective as it allows one to discriminate depolarizing channels associated not with just fixed values of the depolarization factor, but with ranges of it. Additionally, we discovered that a simple modification of one of the commonly used kernels significantly increases the efficiency of this approach. Finally, our numerical findings reveal that the performance of variational methods of channel discrimination depends on the trace of the product of the output states. These findings demonstrate that quantum machine learning can be used to discriminate channels, such as those representing physical noise processes.