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UID:meetup-1f285a7075@palaner.app
DTSTAMP:20260802T151359Z
DTSTART:20260802T180000Z
DTEND:20260802T193000Z
SUMMARY:Decoding the Quantum Frontier: AI-Driven Error Correction
DESCRIPTION:Saved in Palaner as: saved\n\nPalaner match: 36% · why: lots of
  people going\n\nTitle : Decoding the Quantum Frontier: AI-Driven Error Co
 rrection\nDate: August 2 2026 Sunday 14:00 - 16:00 EDT\nAbstract:\nThe pat
 h to fault-tolerant quantum computing is fundamentally gated by our abilit
 y to perform real-time Quantum Error Correction (QEC). While surface codes
  provide a robust geometric framework for topological protection\, the cla
 ssical control layer currently faces a scaling crisis. Traditional decodin
 g algorithms\, such as Minimum-Weight Perfect Matching (MWPM)\, are increa
 singly insufficient for large-scale lattices\, struggling with both the ex
 ponential growth of computational complexity and the nuanced\, correlated 
 noise patterns inherent in modern hardware.\nIn this talk\, Dr. Bagherzade
 h and Samira will present a paradigm shift in the QEC stack: replacing rig
 id classical decoders with adaptive\, AI-driven neural architectures. We d
 emonstrate how the 2D lattice geometry of surface codes can be effectively
  treated as a dynamic "image" problem\, allowing for the application of Vi
 sion Transformer (ViT) and sequence-based Transformer models to decode syn
 drome signals. By leveraging self-attention mechanisms\, these neural deco
 ders can identify non-local error correlations that traditional algorithms
  miss\, significantly improving logical error rates at scale.\nBeyond the 
 algorithmic advantages\, we explore the engineering realities of implement
 ing these models within the cryogenic control loop. We discuss techniques 
 such as model distillation\, quantization\, and FPGA-based co-processor in
 tegration\, aimed at achieving the sub-microsecond latency required for re
 al-time error correction. We conclude by framing the future of quantum com
 puting not merely as a quest for more physical qubits\, but as an optimiza
 tion challenge for the classical "classical brain" that keeps those qubits
  alive. Attendees will gain an understanding of how integrating LLM-inspir
 ed architectures into the quantum stack is the essential\, missing compone
 nt for transitioning from noisy intermediate-scale devices to utility-scal
 e fault tolerance.\nSpeakers:\nNader Bagherzadeh\, an IEEE Fellow\, is a P
 rofessor of Computer Engineering in the Department of Electrical Engineeri
 ng and Computer Science at the University of California\, Irvine\, where h
 e served as Department Chair from 1998 to 2003. Since earning his Ph.D. fr
 om the University of Texas at Austin in 1987\, he has pioneered research i
 n microarchitecture hardware/software optimization\, reconfigurable comput
 ing\, Network-on-Chip\, and 3D IC systems. His current work focuses on nex
 t-generation frontiers\, including machine learning accelerators and quant
 um computing. Professor Bagherzadeh has authored more than 350 articles in
  leading peer-reviewed journals and conferences.\nSamira Sayedsalehi is a 
 PhD candidate in Electrical Engineering and Computer Science at the Univer
 sity of California\, Irvine\, advised by Professor Nader Bagherzadeh. Her 
 research focuses on quantum error correction\, particularly machine learni
 ng approaches to decoding for fault tolerant quantum computing. She has pu
 blished across quantum computing and nanoelectronics\, and her broader int
 erests include quantum machine learning. She has held visiting researcher 
 experience at Universidad Complutense de Madrid and has served as a teachi
 ng assistant and instructor at UCI.
URL:https://www.meetup.com/washington-quantum-computing-meetup/events/31550
 4045/
CATEGORIES:palaner,saved
STATUS:TENTATIVE
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