Friendly Introduction to Quantum and Quantum-Inspired Machine Learning скачать в хорошем качестве

Friendly Introduction to Quantum and Quantum-Inspired Machine Learning

This talk introduces quantum and quantum-inspired machine learning from a practical perspective. We start with a brief overview of classical ML, then move to quantum-inspired approaches based on tensor networks, which already enable efficient representations and compression on classical hardware. Next, we cover quantum machine learning, including variational circuits, quantum kernels, and hybrid quantum–classical workflows, highlighting where quantum models may assist (rather than replace) classical ML. We close with real-world application outlooks for both paradigms and open questions around scalability, and future perspectives, including whether demonstrating quantum advantage should be the only goal of quantum machine learning.