Quantum Computing for Healthcare Digital Twin Systems

TL;DR

This paper reviews the potential of Quantum Digital Twins in healthcare, highlighting their advantages, current challenges, and future research directions for secure, scalable, and clinically trusted quantum-enabled healthcare systems.

Contribution

It provides a comprehensive analysis of QDTs in healthcare, identifying key challenges and proposing strategies for their development and adoption.

Findings

Quantum Digital Twins can enhance healthcare system performance.

Current limitations include hardware constraints and integration issues.

Future research should focus on security, scalability, and clinical trust.

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.