Quantum machine learning channel discrimination

• URL: http://arxiv.org/abs/2206.09933v1
• Date: Mon, 20 Jun 2022 18:00:05 GMT
• Title: Quantum machine learning channel discrimination
• Authors: Andrey Kardashin and Anna vlasova and Anastasia Pervishko and Dmitry Yudin and Jacob Biamonte
• Abstract summary: In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels. This work studies applications of variational quantum circuits and machine learning techniques for discriminating such channels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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