All are welcome to attend the thesis seminar of Anthony Smaldone of the Batista Group.
Title: Development of Quantum Machine Learning Methods Applied to Drug Discovery
Abstract
This thesis seminar explores how quantum computing and artificial intelligence can work together to accelerate drug discovery. It introduces new methods that serve as quantum analogs of efficient convolutional neural networks, achieving state-of-the-art performance among quantum neural networks and providing architectures designed to handle high-channel data, a critical feature for chemical and biological applications. The seminar also examines how quantum circuits can be leveraged for efficient inner product computation, applying this capability to predict drug toxicity and to establish the first hybrid quantum-classical transformer model with a mathematically rigorous, attention-preserving mechanism for molecular generation. Together, these developments advance the applications of quantum machine learning while addressing key challenges in chemistry and pharmaceutical research.
