BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Guillaume Verdon (X (formerly Google X)\, CA\, USA) DTSTART:20200619T000000Z DTEND:20200619T010000Z DTSTAMP:20260423T041752Z UID:UTSQSI/10 DESCRIPTION:Title: Quantum-probabilistic Generative Models and Variational Quantum Thermaliza tion\nby Guillaume Verdon (X (formerly Google X)\, CA\, USA) as part o f Centre for Quantum Software and Information Seminar Series\n\n\nAbstract \nWe introduce a new class of generative quantum-neural-network-based mode ls called Quantum Hamiltonian-Based Models (QHBMs). In doing so\, we estab lish a paradigmatic approach for quantum-probabilistic hybrid variational learning of quantum mixed states\, where we efficiently decompose the task s of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition\, we int roduce the Variational Quantum Thermalizer (VQT) algorithm for generating the thermal state of a given Hamiltonian and target temperature\, a task f or which QHBMs are naturally well-suited. The VQT can be seen as a general ization of the Variational Quantum Eigensolver (VQE) to thermal states: we show that the VQT converges to the VQE in the zero temperature limit. We provide numerical results demonstrating the efficacy of these techniques i n several illustrative examples. In addition to the introduction to the th eory and applications behind these models\, we will briefly walk through t heir numerical implementation in TensorFlow Quantum.\n\nHosted by Chris Fe rrie\, UTS Centre for Quantum Software and Information\n LOCATION:https://researchseminars.org/talk/UTSQSI/10/ END:VEVENT END:VCALENDAR