Dutta Tarun, Jin Alex, Huihong Clarence Liu, Latorre José Ignacio, Mukherjee Manas
School of Physics, University of Hyderabad, Hyderabad 500046, India.
Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore.
iScience. 2025 Jul 9;28(8):113058. doi: 10.1016/j.isci.2025.113058. eCollection 2025 Aug 15.
Advancements in classical computing have propelled machine learning applications, yet inherent limitations persist in terms of energy, resource, and speed. Quantum machine learning offers a promising avenue to overcome these limitations but poses its own hurdles. This experimental study investigates the training limits of a real quantum-classical hybrid system on an ion-trap platform using supervised protocols. It addresses the challenges of coupling ion-trap systems with classical processors and highlights the effectiveness of genetic algorithms for NISQ devices and complex binary classification tasks with many local minima. The analysis reveals why gradient-based methods may not be ideal in the NISQ era. Our results provide insights into optimizing hybrid quantum-classical systems, emphasizing the importance of training strategies and hardware design. This work not only enhances understanding of such systems but also illustrates their practical potential in solving real-world problems without relying on classical simulators, bridging the gap between quantum and classical computing paradigms.
经典计算的进步推动了机器学习应用的发展，然而在能源、资源和速度方面仍然存在固有的局限性。量子机器学习为克服这些局限性提供了一条有前景的途径，但也带来了自身的障碍。这项实验研究使用监督协议，研究了离子阱平台上实际量子 - 经典混合系统的训练极限。它解决了将离子阱系统与经典处理器耦合的挑战，并突出了遗传算法对于含噪声中等规模量子（NISQ）设备以及具有许多局部极小值的复杂二元分类任务的有效性。分析揭示了为什么基于梯度的方法在NISQ时代可能并不理想。我们的结果为优化量子 - 经典混合系统提供了见解，强调了训练策略和硬件设计的重要性。这项工作不仅增进了对这类系统的理解，还展示了它们在不依赖经典模拟器解决现实世界问题中的实际潜力，弥合了量子和经典计算范式之间的差距。
