Quantum Computing for Healthcare Digital Twin Systems

2026-02-17

AI Summary

This study addresses the limitations of classical medical digital twin systems—particularly their poor scalability, computational inefficiency, and inadequate security—which hinder real-time intelligent diagnosis and treatment. The work systematically examines the core challenges of implementing quantum digital twins in healthcare, including constraints imposed by current quantum hardware, the design of hybrid classical-quantum architectures, mechanisms for cloud-based quantum access, and the establishment of clinical trustworthiness. By integrating advances in quantum computing, post-quantum security, and hybrid system engineering, the paper proposes a coherent technical pathway oriented toward safety, reliability, and clinical feasibility. This research offers a theoretical framework and a forward-looking roadmap for the development of next-generation intelligent healthcare systems grounded in quantum-enhanced digital twin technologies.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The growing complexity of healthcare systems requires advanced computational models for real-time monitoring, secure data exchange, and intelligent decision-making. Digital Twins (DTs) provide virtual representations of physical healthcare entities, enabling continuous patient monitoring and personalized care. However, classical DT frameworks face limitations in scalability, computational efficiency, and security. Recent studies have introduced Quantum Digital Twins (QDTs) to enhance performance through quantum computing, addressing challenges such as quantum-resistant security and efficient task offloading in healthcare environments. Despite these advances, most existing QDT models remain constrained by fundamental challenges related to quantum hardware limitations, hybrid classical-quantum system integration, cloud-based quantum access, scalability, and clinical trust. This paper provides a comprehensive review of QDTs for healthcare, with a particular focus on identifying and analyzing the key challenges that currently hinder their real-world adoption. Furthermore, it outlines critical research directions and enabling strategies aimed at advancing the development of secure, reliable, and clinically viable quantum digital twin systems for next-generation healthcare applications.

Problem

• Quantum Digital Twins
• Healthcare
• Quantum Computing
• Scalability
• Clinical Trust

Innovation

• Quantum Digital Twin
• Healthcare
• Quantum Computing
• Hybrid Classical-Quantum Systems
• Clinical Trust