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        "event_title":"Probability and Analysis Seminar: Tightness of and counterexamples to several quantum estimates",
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        "combined_title":"Probability and Analysis Seminar: Tightness of and counterexamples to several quantum estimates: Alexander Volberg (MSU)",
        "event_subtitle":"Alexander Volberg (MSU)",
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        "event_type_id":"21",
        "description":"Abstract: Recently the TCS query learning ala Nisan--Mansur--Linial got the boost from a harmonic analysis random algorithm. This problem has a non-commutative version where one needs to learn a very big matrix (so-called d-local hamiltonians) from  a few random queries.\nThe corresponding harmonic analysis results have a drawback that it is still not known how tight they are. We will discuss this and show tightness for some of them. Interestingly, one such tightness result is closely related to Einstein--Podolsky--Rosen paradox from quantum mechanics. On a close issue: arguably the main problem in understanding the speed up  of quantum algorithms versus classical ones for a certain collection of problems (called Aaronson--Ambainis conjecture and still open) will be discussed too if time permits.",
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        "datetime_modified":"20260729T153011",
        "datetime_start":"20260930T160000",
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        "event_title":"Quantitative propagation of chaos and universality for asymmetric Langevin spin glass dynamics",
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        "combined_title":"Quantitative propagation of chaos and universality for asymmetric Langevin spin glass dynamics: Manuel Arnese",
        "event_subtitle":"Manuel Arnese",
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        "description":"We obtain quantitative estimates on quenched propagation of chaos for Langevin spin glass dynamics with i.i.d. disorder. Prior work in the case of Gaussian disorder established the qualitative convergence of the law of a single spin to a deterministic McKean--Vlasov limit. We prove convergence rates in expected Wasserstein distance and quantitative concentration rates for Lipschitz observables under the assumption that the disorder satisfies the T_2 inequality. The proof uses a coupling argument, together with techniques from concentration of measure, filtering theory, and Malliavin calculus.",
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