Decoding the Quantum Frontier: AI-Driven Error Correction

Title : Decoding the Quantum Frontier: AI-Driven Error Correction
Date: August 2 2026 Sunday 14:00 - 16:00 EDT
Abstract:
The path to fault-tolerant quantum computing is fundamentally gated by our ability to perform real-time Quantum Error Correction (QEC). While surface codes provide a robust geometric framework for topological protection, the classical control layer currently faces a scaling crisis. Traditional decoding algorithms, such as Minimum-Weight Perfect Matching (MWPM), are increasingly insufficient for large-scale lattices, struggling with both the exponential growth of computational complexity and the nuanced, correlated noise patterns inherent in modern hardware.
In this talk, Dr. Bagherzadeh and Samira will present a paradigm shift in the QEC stack: replacing rigid classical decoders with adaptive, AI-driven neural architectures. We demonstrate how the 2D lattice geometry of surface codes can be effectively treated as a dynamic "image" problem, allowing for the application of Vision Transformer (ViT) and sequence-based Transformer models to decode syndrome signals. By leveraging self-attention mechanisms, these neural decoders can identify non-local error correlations that traditional algorithms miss, significantly improving logical error rates at scale.
Beyond the algorithmic advantages, we explore the engineering realities of implementing these models within the cryogenic control loop. We discuss techniques such as model distillation, quantization, and FPGA-based co-processor integration, aimed at achieving the sub-microsecond latency required for real-time error correction. We conclude by framing the future of quantum computing not merely as a quest for more physical qubits, but as an optimization challenge for the classical "classical brain" that keeps those qubits alive. Attendees will gain an understanding of how integrating LLM-inspired architectures into the quantum stack is the essential, missing component for transitioning from noisy intermediate-scale devices to utility-scale fault tolerance.
Speakers:
Nader Bagherzadeh, an IEEE Fellow, is a Professor of Computer Engineering in the Department of Electrical Engineering and Computer Science at the University of California, Irvine, where he served as Department Chair from 1998 to 2003. Since earning his Ph.D. from the University of Texas at Austin in 1987, he has pioneered research in microarchitecture hardware/software optimization, reconfigurable computing, Network-on-Chip, and 3D IC systems. His current work focuses on next-generation frontiers, including machine learning accelerators and quantum computing. Professor Bagherzadeh has authored more than 350 articles in leading peer-reviewed journals and conferences.
Samira Sayedsalehi is a PhD candidate in Electrical Engineering and Computer Science at the University of California, Irvine, advised by Professor Nader Bagherzadeh. Her research focuses on quantum error correction, particularly machine learning approaches to decoding for fault tolerant quantum computing. She has published across quantum computing and nanoelectronics, and her broader interests include quantum machine learning. She has held visiting researcher experience at Universidad Complutense de Madrid and has served as a teaching assistant and instructor at UCI.