BAIQO

Bayesian Network Analysis and Inference via Quantum-enhanced Optimization

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Description

A major problem in industry and research is optimizing the planning and execution of clinical trials. In view of the high costs and long development times of drugs, pharmaceutical and biotech companies are looking for more efficient ways to bring drugs through the development phases in a more targeted, faster and safer way, for example by optimizing and accelerating patient recruitment.

The central goal of the BAIQO project is the design, development and evaluation of various quantum algorithms for the optimization of models generated from large data sets using machine learning (so-called Bayesian models).

As a use case, the project is investigating how clinical studies can be optimized. The extent to which different types of quantum algorithms can be used will also be investigated. Machine-derived models for clinical trials are often highly complex with many variables and dependencies between the variables. One research question in the project is therefore to what extent such models can generally be translated into optimization problems. The evaluation on currently available NISQ devices will further clarify whether there is a “quantum advantage” compared to classical approaches for optimizing clinical trials.

Funded by
Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Partners
MERCK KGaA
Contact at the chair
Maximilian Mansky
Website
https://www.quantentechnologien.de