Machine learning of quantum channels on NISQ devices

TL;DR

This paper introduces a neural-network-based method to infer effective quantum channels from experimental data on NISQ devices, enabling better understanding of quantum dynamics without prior knowledge.

Contribution

It presents a novel neural-network algorithm for approximating discrete-time quantum dynamics, applicable to various types of quantum channels on NISQ hardware.

Findings

Successfully inferred quantum channels on IBM quantum processor

Applicable to time-periodic Lindblad and non-unitary subsystem dynamics

Demonstrated potential for diagnosing cross-talk effects

Abstract

World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channels, are implemented on these devices. To systematically infer those, we propose a neural-network algorithm approximating generic discrete-time dynamics through the repeated action of an effective quantum channel. We test our approach considering time-periodic Lindblad dynamics as well as non-unitary subsystem dynamics in many-body unitary circuits. Moreover, we exploit it to investigate cross-talk effects on the ibmq_ehningen quantum processor, which showcases our method as a practically applicable tool for inferring quantum channels when the exact nature of the underlying dynamics on the physical device is not known a priori. While the present approach is tailored for learning Markovian dynamics, we discuss how it can be adapted to also capture generic non-Markovian discrete-time evolutions.