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SUMMARY:Maximum Likelihood Decoding for Quantum Error Correction
DTSTART;VALUE=DATE-TIME:20260720T063000Z
DTEND;VALUE=DATE-TIME:20260720T073000Z
DTSTAMP;VALUE=DATE-TIME:20260725T231500Z
UID:indico-event-5183@indico-tdli.sjtu.edu.cn
DESCRIPTION:Speakers: Feng Pan (潘峰) (Singapore University of Technolog
 y and Design)\n\nHost: Prof. Xin Liu  Venue: TDLI Meeting Room N400Ten
 cent Meeting link: https://meeting.tencent.com/dm/CcYqQPEFY8zm Meeting I
 D: 491786622\, no password Abstract:Quantum error correction is essential
  for bridging the large gap between the error rates of current quantum har
 dware and those required for practical fault-tolerant computation. A centr
 al challenge is decoding: inferring the most probable logical error class 
 from noisy syndrome measurements. While minimum-weight decoding identifies
  a single low-weight error configuration\, maximum-likelihood decoding acc
 ounts for the combined probability of all logically equivalent configurati
 ons and can therefore provide higher decoding accuracy\, albeit at substan
 tially greater computational cost.This talk presents three complementary a
 pproaches to maximum-likelihood decoding. First\, we show how the decoding
  problem for planar codes can be mapped to a statistical-mechanics model w
 hose partition function is evaluated efficiently using the Kac–Ward form
 ulation. Second\, we discuss tensor-network representations that extend ma
 ximum-likelihood decoding to more general code structures\, together with 
 methods for accelerating approximate contraction. Finally\, we examine neu
 ral-network decoders that learn correlations in syndrome histories and ada
 pt to realistic device noise through simulation-based pretraining and expe
 rimental fine-tuning. Results on representative-code benchmarks and experi
 mental quantum-device data illustrate the accuracy\, scalability\, and com
 putational trade-offs of these approaches. Together\, they provide a unifi
 ed perspective on using statistical mechanics\, tensor networks\, and mach
 ine learning to enable practical\, high-accuracy quantum error correction.
  Biography:Feng Pan is an Assistant Professor in the Science\, Mathematic
 s\, and Technology Cluster at the Singapore University of Technology and D
 esign. He received his PhD in theoretical physics from the Institute of Th
 eoretical Physics at the Chinese Academy of Sciences in 2022. Following hi
 s PhD\, he worked as a Research Fellow at the Centre for Quantum Technolog
 ies.His research lies at the intersection of tensor networks\, quantum com
 puting\, statistical physics\, machine learning\, and high-performance sci
 entific computing. His work has been published in leading journals and con
 ferences\, including Nature Computational Science\, Physical Review Letter
 s\, and the International Conference for High Performance Computing\, Netw
 orking\, Storage and Analysis. His notable contributions include algorithm
 s for contracting arbitrary tensor networks\, large-scale classical simula
 tion of quantum circuits\, tensor-network message passing\, physics-inspir
 ed methods for combinatorial optimization\, and exact maximum-likelihood d
 ecoding of quantum error-correcting codes. His research has also been high
 lighted by Science Journal and Phys.org. Beyond academia\, Feng maintains 
 active collaborations with leading technology companies\, including Google
  and NVIDIA.\n\nhttps://indico-tdli.sjtu.edu.cn/event/5183/
LOCATION:Tsung-Dao Lee Institute/N4F-N400 - meeting room (Tsung-Dao Lee In
 stitute)
URL:https://indico-tdli.sjtu.edu.cn/event/5183/
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