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Explore advanced techniques for stabilizing nonequilibrium phases of matter through active feedback mechanisms and partial information processing in this conference talk. Learn how machine learning approaches can be applied to control quantum many-body systems that are driven out of equilibrium, with particular focus on feedback protocols that maintain desired quantum phases. Discover the theoretical foundations behind using incomplete or partial information to design effective stabilization strategies for complex quantum systems. Examine the intersection of quantum learning theory and nonequilibrium statistical mechanics, including how adaptive feedback can counteract decoherence and thermalization processes. Understand the practical implications for quantum hardware implementations and experimental realizations of these stabilization protocols. Gain insights into the latest developments in interactive quantum dynamics and how they relate to broader questions in quantum statistical mechanics and many-body physics.
