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Explore new directions in quantum distribution learning and testing in this hour-long seminar featuring Ryan O'Donnell from Carnegie Mellon University. Delve into efficient algorithms for quantum state tomography, understand the importance of distinguishing between various distance measures in probability theory, and discover quantum-inspired improvements to classical independence testing. Gain insights from O'Donnell's expertise in quantum computation, information theory, and complexity theory as he presents joint work with Steven T. Flammia from Amazon. Suitable for those interested in advanced topics in computer science, mathematics, and quantum information.
