Classical and Quantum Machine Learning approaches for Cellular Engineering

DIPC Seminars

Sara Capponi
IBM Almaden Research Center, USA
Hybrid Seminar: Donostia International Physics Center
Aitzol Garcia-Etxarri
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Classical and Quantum Machine Learning approaches for Cellular Engineering

During the past decade the interest in designing cells has increased considerably due to relevant applications including biotechnology, immunotherapy, and global warming counteraction among others. This rapidly advancing field has witnessed substantial growth owing to the integration of artificial intelligence (AI) and machine learning (ML) approaches. Additionally, there has been tremendous progress in the development of quantum computing hardware and algorithms leading to the expectation that in the near future quantum computers will be capable of performing simulations and machine learning at scales mostly inaccessible to classical computers. In this talk, I will discuss the applications of ML approaches in the main field of cellular engineering and will present some examples from my own research, including predictions of binding affinity trends between two proteins and the discovery of the intrinsic rules that define the different phenotypes of genetically engineered immune cells. Then, I will focus on immune cell design and compared results obtained by using classical and quantum ML approaches.