Quantum Computing for Healthcare Digital Twin Systems

TL;DR

This paper surveys the integration of quantum computing with healthcare digital twins, identifies the key barriers to real-world adoption, and articulates a research agenda to overcome hardware, integration, cloud, security, scalability, and trust challenges. It discusses conceptual QDT architectures and quantum algorithms such as $Shor$, $Grover$, and $QFT$, as well as security enhancements for healthcare data exchange. The authors argue that secure, low-latency, clinically interpretable QDTs are essential prerequisites for deploying quantum-accelerated diagnostics, planning, and training in next-generation healthcare. The work highlights that incremental advances in quantum hardware, hybrid designs, and standardized validation are required to realize clinically viable QDTs.

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.

Figures (3)

• Figure 1: Conceptual overview of digital twin technology in healthcare, illustrating the interaction between the physical system (patient) and its digital twin through real-time data integration, and the main application domains including predictive analytics, clinical workflow optimization, and training and simulation.
• Figure 2: Conceptual architecture of a digital twin–quantum computing framework. Data flows from the physical system through the digital twin, data layer, optimization engine, and quantum simulator to generate a solution, with a closed-loop connection from the decision output back to the digital twin.
• Figure 3: Conceptual illustration of the impact of QDTs across major healthcare sectors.