Quantum-assisted federated intelligent diagnosis algorithm with variational training supported by 5G networks.

Area of Science:

• Intelligent Healthcare
• Artificial Intelligence in Medicine
• Quantum Computing Applications

Background:

• Conventional machine learning in healthcare faces privacy, update, and training time challenges due to data sharing reluctance.
• Sensitive medical data sharing is hindered by privacy concerns and potential noise, impacting AI model development.
• The integration of Artificial Intelligence (AI) into healthcare necessitates overcoming these traditional machine learning limitations.

Purpose of the Study:

• To propose and apply a Quantum-Assisted Federated Intelligent Diagnosis Algorithm (QuAFIDA) to real medical data.
• To leverage 5G and Internet of Medical Things (IoMT) for synchronized, real-time model training without disrupting clinical applications.
• To enhance patient data privacy and improve the efficiency of AI model training in healthcare settings.

Main Methods:

• Developed a Quantum-Assisted Federated Intelligent Diagnosis Algorithm (QuAFIDA) utilizing a nested loop heuristic approach.
• Employed a beta-variational quantum eigensolver (β-VQE) in the inner loop to approximate expectation values.
• Trained QuAFIDA in the outer loop to minimize relative entropy, balancing privacy and training urgency.

Main Results:

• Demonstrated that QuAFIDA effectively acquires low-rank states through low-rank representations.
• Validated the algorithm's application on real medical data, showcasing its practical utility.
• Confirmed the synergy between AI, 5G, and quantum computing for intelligent healthcare advancements.

Conclusions:

• QuAFIDA offers a novel solution to privacy and efficiency challenges in AI-driven healthcare.
• The integration of 5G and quantum-assisted federated learning represents a significant advancement for intelligent healthcare systems.
• This approach paves the way for more secure, efficient, and responsive medical diagnostic tools.