Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era.

Area of Science:

• Quantum Computing
• Machine Learning
• Computational Science

Background:

• Classical computing faces energy, resource, and speed limitations for machine learning.
• Quantum machine learning (QML) offers potential solutions but presents unique challenges.
• Hybrid quantum-classical systems aim to leverage both computational paradigms.

Purpose of the Study:

• Investigate the training limitations of a real quantum-classical hybrid system on an ion-trap platform.
• Evaluate the effectiveness of different training protocols for QML.
• Address challenges in integrating quantum hardware with classical processors.

Main Methods:

• Experimental study on a real ion-trap quantum-classical hybrid system.
• Utilized supervised learning protocols.
• Employed genetic algorithms for training and compared with gradient-based methods.

Main Results:

• Genetic algorithms are effective for noisy intermediate-scale quantum (NISQ) devices and complex binary classification tasks.
• Identified limitations of gradient-based methods in the NISQ era.
• Demonstrated successful training without classical simulators.

Conclusions:

• Optimizing hybrid quantum-classical systems requires careful consideration of training strategies and hardware design.
• Genetic algorithms offer a viable approach for QML on current NISQ hardware.
• This research bridges the gap between quantum and classical computing for practical applications.