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SUMMARY:Statistical Signal Processing for Quantum Error Mitigation - Prof.
  Dror Baron\, North Carolina State University
DTSTART:20251015T130000Z
DTEND:20251015T140000Z
UID:TALK235936@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:In the noisy intermediate-scale quantum (NISQ) era\, quantum e
 rror mitigation (QEM) is essential for producing reliable outputs from qua
 ntum circuits. We present a statistical signal processing approach to QEM 
 that estimates the most likely noiseless outputs from noisy quantum measur
 ements. Our model assumes that circuit depth is sufficient for depolarizin
 g noise\, producing corrupted observations that resemble a uniform distrib
 ution alongside classical bit-flip errors from readout. Our method consist
 s of two steps: a filtering stage that discards uninformative depolarizing
  noise and an expectation-maximization (EM) algorithm that computes a maxi
 mum likelihood (ML) estimate over the remaining data. We demonstrate the e
 ffectiveness of this approach on small-qubit systems using circuit simulat
 ions in Qiskit and IBM quantum processing unit (QPU) data\, and compare it
 s performance to contemporary statistical QEM techniques. We also show tha
 t our method scales to larger qubit counts using synthetically generated d
 ata consistent with our noise model. These results suggest that principled
  statistical methods can offer scalable and interpretable solutions for qu
 antum error mitigation in realistic NISQ settings.  Finally\, while this t
 alk solves a problem that appears on quantum computers\, the solution tech
 nique does not require a quantum background. People who work in informatio
 n theory\, signal processing\, and machine learning should be able to foll
 ow and appreciate the topic.
LOCATION:MR5\, CMS Pavilion A
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