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SUMMARY:QSE Quantum Seminar: Computing with Physical Systems: Opportunitie
 s and Fundamental Limits
DTSTART:20250501T120000
DTEND:20250501T133000
DTSTAMP:20260413T100159Z
UID:3bf968534aaa6c2e1f4d429d820efd323b46b362f6fe793c0cbd04cb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Hakan Türeci\nPlease join us for the QSE Center Quantum Se
 minar with  Hakan Türeci from Princeton Materials Institute (PMI)\, 
 who will give the talk "Computing with Physical Systems: Opportunities an
 d Fundamental Limits" on Thursday May 1st.\nLocation: CM 1 121.\n\nPizzas
  will be available before the seminar at 12:00. All PhDs\, postdocs\, stu
 dents\, and PIs are welcome to join us.\n\nTITLE: "Computing with Physica
 l Systems: Opportunities and Fundamental Limits"\n\nABSTRACT:\nRecent stri
 des in machine learning have shown that computation can be performed by pr
 actically any controllable physical system that responds to physical stimu
 li encoding data [1]. This perspective opens new frontiers for computation
 al approaches using Physical Neural Networks (PNNs) [2\, 3\, 4] and provid
 es a framework to deepen our understanding of their biological counterpart
 s—neural circuits in living organisms. To fully leverage this potential\
 , PNNs must be trained with a nuanced awareness of the physical nature of 
 signal and noise\, where signal is defined relative to the specific comput
 ational task. This perspective aligns closely with approaches to determini
 ng fundamental limits in sensing but extends these ideas to a new level to
  encompass broader computational opportunities. I will share some perspect
 ives on how we approach this new domain of inquiry and some recent results
 .\nBased on work with Fangjun Hu\, Saeed A. Khan\, Gerasimos Angelatos\, M
 arti Vives\, Esin Türeci\, Graham E. Rowlands\, Guilhem J. Ribeill\, Nich
 olas Bronn.\n\nBIO:\n​​​​​​​Hakan Türeci is a theoretical 
 physicist with interests in research problems that frequently intersect qu
 antum optics\, quantum information science\, condensed matter physics\, ph
 otonics\, and lasers. The overarching themes in his group's research rev
 olve around non-equilibrium collective phenomena in optical and microwave 
 platforms. Much of their work is inspired by the possibilities offered b
 y experimentally accessible physics in near-term devices for computing\, s
 imulation\, machine learning and signal processing.\n\n[1] Aspen Center fo
 r Physics Winter Conference\, Computing with Physical Systems\, https://c
 omputingwithphysicalsystems.com/2024/\n\n[2] F. Hu et al. `Tackling Sampli
 ng Noise in Physical Systems for Machine Learning Applications: Fundamenta
 l Limits and Eigentasks." Phys. Rev. X 13\, 041020 (2023).\n\n[3] S. A. Kh
 an et al.\, `A neural processing approach to quantum state discrimination"
 \, arxiv:2409.03748.\n\n[4] F. Hu et al. `Overcoming the Coherence Time Ba
 rrier in Quantum Machine Learning on Temporal Data"\, Nature Commun. 15\, 
 7491 (2024).\n\n 
LOCATION:CM 1 121 https://plan.epfl.ch/?room==CM%201%20121
STATUS:CONFIRMED
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