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UID:20260914T200156EDT-5747G6vz2C@132.216.98.100
DTSTAMP:20260915T000156Z
DESCRIPTION:\n	Virtual Informal Systems Seminar (VISS)\n\n	Centre for Intelli
 gent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Dec
 isions (GERAD)\n\n\nSpeaker: Estelle Inack – Perimeter Institute\, Canada 
 \n\n\n	Webinar link\n		Webinar ID: 910 7928 6959\n		Passcode: VISS\n\n	Abstract:
 \n\n	Many important challenges in science and technology can be cast as opt
 imization problems. When viewed in a statistical physics framework\, these
  can be tackled by simulated annealing\, where a gradual cooling procedure
  helps search for groundstate solutions of a target Hamiltonian. While pow
 erful\, simulated annealing is known to have prohibitively slow sampling d
 ynamics when the optimization landscape is rough or glassy. Here we show t
 hat by generalizing the target distribution with a parameterized model\, a
 n analogous annealing framework based on the variational principle can be 
 used to search for groundstate solutions. Modern autoregressive models suc
 h as recurrent neural networks provide ideal parameterizations since they 
 can be exactly sampled without slow dynamics even when the model encodes a
  rough landscape. We implement this procedure in the classical and quantum
  settings on several prototypical spin glass Hamiltonians\, and find that 
 it significantly outperforms traditional simulated annealing in the asympt
 otic limit\, illustrating the potential power of this yet unexplored route
  to optimization.\n\n	Bio:\n\n	Estelle Inack is the first recipient of the F
 rancis Allotey Fellowship\, which honours the late distinguished Ghanaian 
 mathematician\, at the Perimeter Institute in Waterloo\, Canada. She is wo
 rking at the intersection of quantum computing and artificial intelligence
  at the Perimeter institute Quantum Intelligence Lab. Her research focuses
  in developing quantum-inspired algorithms to tackle real-world optimizati
 on problems using state-of-art machine learning techniques. Estelle obtain
 ed an MSc degree in Physics at the University of Buea (2013)\, a postgradu
 ate diploma in Condensed Matter Physics at ICTP (2014) and a joint PhD deg
 ree in Statistical Physics from ICTP and SISSA (2018).\n\n
DTSTART:20220218T150000Z
DTEND:20220218T160000Z
LOCATION:CA\, ZOOM
SUMMARY:Variational neural annealing 
URL:https://www.mcgill.ca/cim/channels/event/variational-neural-annealing-3
 36543
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