Launched in June 2024, the MMD TechHub brings together expertise from all eight Faculty of Science institutes to accelerate molecular and materials discovery. It combines research in chemistry, physics, and life sciences with artificial intelligence, computational science, data science, and quantum information science.

The new Q-SIM4MMD initiative adds four seed funding projects to the MMD TechHub, embedded in different institutes. Additionally, the newly established Q-Sim expertise group will work with the Q-SIM4MMD seed funding projects to grow expertise in applications in Quantum Simulation. The group will also link the projects to the wider quantum computing ecosystem, including QuSoft.

Each project will receive €200,000 in seed funding over two years. Beyond their immediate research goals and with the help of the MMD TechHub, the projects will work on knowledge transfer, potential applications and follow-up funding, including public–private partnerships.

Quantum machine learning for molecular and material design

Alberto Pérez de Alba Ortíz (IvI/HIMS) and Clelia de Mulatier (IvI/IoP)

Experiments and simulations in molecular and material design can be costly and time-consuming. Bayesian optimization helps researchers decide which experiment or simulation to run next, using predictive models that also estimate uncertainty. This project will develop and test quantum-circuit-based kernels: components of these models that determine how similarities between data points are represented.

The team will explore which circuit designs work best for different molecular properties and how to keep models sufficiently simple to avoid overfitting. Potential applications include studying how pollutants such as PFAS and nanoplastics bind to human proteins, and designing materials that capture unwanted substances.

Exploring quantum computing in bioinformatics

Aalt-Jan van Dijk and Anna Heintz Buschart (SILS)

The team will examine whether quantum algorithms can help address demanding problems in biological data analysis. Quantum prototypes will be integrated into existing analytical workflows and benchmarked against leading classical methods. 

One research direction concerns enzyme specificity: understanding which substances an enzyme acts on and which products it produces. The project will explore quantum and hybrid quantum–classical approaches to tasks such as searching for binding configurations and estimating enzyme–substrate interactions. A second direction focuses on DNA sequence analysis, including microbiome profiling and human–microbiome interactions. 

Towards safer and more sustainable molecules

Saer Samanipour, Bernd Ensing (HIMS) and Antonia Praetorius (IBED)

How a molecule is represented computationally influences what a model can predict about it. Existing representations can struggle to capture the full complexity of molecular structure and electronic interactions, while more detailed approaches can be computationally demanding.

This project will investigate whether quantum methods can support topology-based approaches and provide alternative molecular representations. The longer-term aim is to improve the screening of large chemical databases and support the discovery of molecules designed with safety and sustainability in mind.

Testing quantum simulations against established methods

Philippe Corboz and Arghavan Safavi Naini (IoP), together with Natalia Chepiga (Oxford University)

The team will benchmark quantum simulations of materials against state-of-the-art numerical methods, particularly tensor networks. They will study two-dimensional material models, including Hubbard models used to describe interacting electrons, as well as the long-time dynamics of quantum spin chains. These comparisons will help establish how quantum simulation results measure up against strong classical benchmarks and identify where further development is needed.