Practicality of training a quantum-classical machine in the NISQ era

TL;DR

This paper investigates the practical training of quantum-classical hybrid systems on NISQ devices, emphasizing the robustness of genetic algorithms over gradient-based methods for noisy, complex quantum machine learning tasks.

Contribution

It provides experimental insights into training hybrid quantum-classical systems on ion trap platforms, highlighting the effectiveness of genetic algorithms in noisy environments and analyzing limitations of gradient-based optimizers.

Findings

Genetic algorithms are robust in noisy NISQ environments.

Gradient-based optimizers may be unsuitable for NISQ-era quantum training.

Hybrid systems can be practically trained without classical simulation.

Abstract

Advancements in classical computing have significantly enhanced machine learning applications, yet inherent limitations persist in terms of energy, resource and speed. Quantum machine learning algorithms offer a promising avenue to overcome these limitations but poses its own hurdles. This experimental study explores the limits of training a real experimental quantum classical hybrid system using supervised training protocols, on an ion trap platform. Challenges associated with ion trap-coupled classical processors are addressed, highlighting the $robustness$ of the genetic algorithm as a classical optimizer in navigating the noisy channels of NISQ-devices and the complex optimization landscape inherent in binary classification problems with many local minima. We intricately discuss why gradient-based optimizers may not be suitable in the NISQ era through a thorough analysis. These findings contribute insights into the performance of quantum-classical hybrid systems, emphasizing the significance of efficient training strategies and hardware considerations for practical quantum machine learning applications. This work not only advances the understanding of hybrid quantum-classical systems but also underscores the potential impact on real-world challenges through the convergence of quantum and classical computing paradigms operating without the aid of classical simulators.