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

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.

30 pages, 23 figures