Merck KGaA, Darmstadt, Germany, to Lead BAIQO Quantum Computing Project Funded by the German Federal Ministry of Education and Research

•  The QAR-Lab at Ludwig-Maximilians University in Munich, Germany, is a partner in this joint project
•  Partners want to use quantum computing models to optimize clinical studies

Merck KGaA, Darmstadt, Germany, a leading science and technology company, today announced today announced its membership of the research project BAIQO (Bayesian Network Analysis and Inference via Quantum-assisted Optimization), which is being funded by the German Federal Ministry of Education and Research (BMBF). This three-year project will be carried out in collaboration with the Quantum Applications & Research Laboratory (QAR-Lab) at Ludwig-Maximilians University in Munich (LMU), Germany. This research project focuses on creating a basis for the use of quantum computing in the modeling of clinical studies. Together, the partners will investigate the potentials of various quantum algorithms for optimizing models generated with the aid of machine learning from large data sets.

“The algorithms are to be integrated into our existing optimization platform and investigated. Together, we are approaching the topic of how drug candidates can move through clinical development more purposefully, more quickly, more safely, and of course, more sustainably,” said Thomas Ehmer, project manager on the side of Merck KGaA, Darmstadt, Germany. “Of course, BAIQO offers the potential for new innovative jobs in this technology field.”

Informatics Professor and Institute Chair Claudia Linnhoff-Popien, who heads the QAR-Lab at LMU, is convinced by the BAIQO project: “We at the QAR-Lab see enormous application potential for quantum computing in the optimization of clinical trials. With our many years of expertise in the areas of artificial intelligence and quantum computing, we want to support Merck KGaA, Darmstadt, Germany, in the development and implementation of beneficial algorithms.”

Machine-derived models for clinical studies (known as Bayesian models) are often highly complex, with a very large number of variables and dependencies between those variables. The research partners want to evaluate the extent to which these kinds of models can generally be translated into optimization problems to define the best possible parameter distribution for modeling successful clinical trials.

A further question that the BAIQO project aims to answer is the extent to which different kinds of quantum algorithms can be applied under the existing limitations of current quantum computing hardware, i.e. so-called NISQ devices (NISQ: noisy intermediate-scale quantum). The evaluation of currently available NISQ devices will also clarify whether a “quantum advantage” exists compared with classic approaches for optimizing clinical trials.

The BMBF will fund 73.3% of the € 1.5 million project volume via the “Application Network for Quantum Computing” funding announcement, a measure for implementing the government program “Quantum Technologies – From basic research to market”.