1. Part One will provide an accessible overview of QNNs and why they matter, focusing on their unique ability to handle sparse or incomplete datasets and uncover correlations that classical models often miss. Through examples from literature and real-world case studies, participants will see how QNNs can open new possibilities for discovery and prediction.
2. Part Two will take a deeper dive into quantifying how QNNs compare to classical neural networks, where they have potential performance advantages, and what their integration into enterprise workflows could look like. We will explore both the opportunities and current limitations, while engaging participants in mapping out problems in their own domains that might benefit from this emerging approach.
Check out the incredible speaker line-up to see who will be joining Cierra.
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