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X-WR-CALNAME;VALUE=TEXT:Seminar: Jordan Cotler (Harvard University), Quantum-enhanced Learning using a Quantum Memory
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SUMMARY:Seminar: Jordan Cotler (Harvard University), Quantum-enhanced Learning using a Quantum Memory
DESCRIPTION:<p>	<strong>Title:</strong> Quantum-enhanced Learning using a Quantum Memory</p><p>	Zoom link: <a href="https://harvard.zoom.us/j/779283357?pwd=MitXVm1pYUlJVzZqT3lwV2pCT1ZUQT09" title="">https://harvard.zoom.us/j/779283357?pwd=MitXVm1pYUlJVzZqT3lwV2pCT1ZUQT09</a></p><p>	<drupal-media data-entity-type="media" data-entity-uuid="4223c5aa-9385-47c7-ad69-d1f1649b6b4d" alt="Jordan Cotler" data-view-mode="hwp_small"></drupal-media></p><p>	<strong>Abstr</strong><strong>act: </strong> We study the power of quantum memory for learning properties of experimental systems, in particular quantum systems and their dynamics.  By synthesizing and generalizing recent approaches to quantum learning algorithms and quantum algorithmic measurements, we provide a new framework for proving exponential complexity separations between interactive protocols with and without quantum memories.  We will establish (i) exponential separations between algorithms with and without quantum memory for purity testing, distinguishing scrambling and depolarizing evolutions, as well as uncovering symmetry in physical dynamics; (ii) exponential tradeoffs between quantum memory and sample complexity for estimating Pauli observables; and (iii) tight bounds (up to logarithmic factors) for shadow tomography without a quantum memory.  Some of our complexity advantages have been realized on Google's Sycamore processor, demonstrating a real-world advantage for learning algorithms with a quantum memory.</p>
LOCATION:  Zoom
STATUS:CONFIRMED
DTSTART:20211019T133000Z
DTEND:20211019T133000Z
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