Trainability of a quantum-classical machine in the NISQ era
arxiv(2024)
摘要
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 bring along their own challenges. This
experimental study explores the limits of trainability of a real experimental
quantum classical hybrid system implementing supervised training protocols, in
an ion trap platform. Challenges associated with ion trap-coupled classical
processor are addressed, highlighting the robustness of the genetic algorithm
as a classical optimizer in navigating complex optimization landscape inherent
in binary classification problems with many local minima. Experimental results,
focused on a binary classification problem, reveal the superior efficiency and
accuracy of the genetic algorithm compared to gradient-based optimizers. We
intricately discuss why gradient-based optimizers may not be suitable in the
NISQ era through 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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