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Join a Qiskit Quantum Seminar where Zoltán Zimborás explores the intersection of quantum computing and machine learning through problem-informed graphical quantum generative learning. Discover how probabilistic graphical models can be leveraged to enhance quantum machine learning algorithms by incorporating structured problems and inductive bias. Learn about a novel quantum circuit Born machine Ansatz designed for learning joint probability distributions using Markov networks, and understand how this approach outperforms problem-agnostic circuits. Examine the analysis of trainability properties in Markov networks and explore the potential for quantum advantage in generative learning. Benefit from Zimborás's extensive expertise as the head of the Quantum Computing and Information Research Group at the Wigner Research Centre for Physics and senior researcher at Algorithmiq, as he shares insights from his research and practical implementation experience with quantum algorithms.
