Digital Twins for Biological Systems

Creating digital twins of biological systems, such as organs, tissues, or cellular networks, requires the integration of diverse and detailed data, such as molecular profiles and physiological parameters. These models are computationally demanding, especially when they need to simulate processes in real time or at a high level of detail. Current classical computing approaches often face limitations in scaling, speed, or accuracy, which can restrict the practical use of digital twins in clinical and research settings.

Quantum computing offers new possibilities for simulating complex biological processes by handling large, high-dimensional datasets more efficiently. Quantum algorithms could support tasks such as molecular modelling or system-level simulations within a digital twin. When combined with classical computing in hybrid systems, quantum computing may improve both the speed and resolution of these models. This can make it more feasible to update digital twins in near real-time and incorporate a wider range of biological detail.

This could improve the accuracy and responsiveness of simulations used in diagnostics, treatment planning, and research. For example, clinicians could explore treatment options in a simulated environment before applying them to a patient. In research, more detailed models could support the development of new therapies. While still at an early stage, this approach could gradually enhance personalised medicine and lead to more efficient, data-driven decision-making.