Friendly Introduction to Quantum and Quantum-Inspired Machine Learning

Dr. Ema Puljak Bio: Ema Puljak is a Quantum Algorithms Expert at PlanQC, working on quantum and quantum-inspired machine learning for real-world applications. She completed a collaborative PhD between CERN, the Barcelona Supercomputing Center, and Universitat Autònoma de Barcelona, focusing on improving anomaly detection methods using quantum and tensor-network techniques for medical imaging and high-energy physics. Passionate about science communication, she regularly delivers accessible talks and tutorials on quantum computing, tensor networks, and quantum machine learning. TITLE: Friendly Introduction to Quantum and Quantum-Inspired Machine Learning Abstract: 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.