Many strategies, one goal: hardware implementable quantum Sci-ML

Abstract:

(Stochastic) Differential Equations (DEs) and graph-theoretic problems are ubiquitous, but solving intricate them can be computationally challenging due to their scale and complexity. As such, recently Scientific Machine Learning (SciML) approaches were explored to target them efficiently, seamlessly embed actual data where available, and exploit generalization properties typical of ML models. We will introduce recent developments in the field of (graph) Quantum Machine Learning, focusing in particular on Differentiable Quantum Circuits (DQC) and graph-based approaches (e.g. Quantum Evolution Kernels) native to neutral atom architectures. Finally, we will cover some recent developments beyond purely variational quantum algorithms for PDEs, i.e. quantum iterative methods as well as algorithms operating on features in the latent space. An emphasis will be on showcasing results of their application in various types of physics and engineering problems, towards industrial-scale relevant applications.

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- Link Zoom per pubblico online: https://univr.zoom.us/j/94533093158

Meeting ID: 945 3309 3158