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

• URL: http://arxiv.org/abs/2401.12089v2
• Date: Mon, 17 Feb 2025 11:47:09 GMT
• Title: Practicality of training a quantum-classical machine in the NISQ era
• Authors: Tarun Dutta, Alex Jin, Clarence Liu Huihong, J I Latorre, Manas Mukherjee,
• Abstract summary: This 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 in navigating the noisy channels of NISQ-devices. 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.
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• 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.

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