analiza aiqbench
This research advances fault-tolerant quantum computing by applying sequential belief propagation to overcome decoding limitations in QLDPC codes, such as short cycles and convergence failures. By optimizing these error-correction strategies with reinforcement learning techniques, the team aims to improve information extraction reliability from qubits without increasing computational complexity. This work represents a significant step toward scalable, practical quantum hardware architectures supported by recent NSF grants.
Arizona State University Receives NSF Grants for Quantum Code Decoding Approach

ASU researchers are improving performance of quantum low-density parity-check (QLDPC) codes, using reinforcement learning to optimize belief-propagation decoding.
Researchers from Arizona State University, The University of Texas at Arlington, and New Mexico State University are testing a new approach to decoding quantum low-density parity-check codes, a critical step toward reliable quantum computing. The team, including Mohsen Moradi, Taejoon Kim, Rémi Chou, and David Mitchell, is moving beyond variable-node-by-variable-node reinforcement learning to a sequential belief propagation method. Belief-propagation decoding for quantum low-density parity-check codes is attractive due to its low complexity, but its performance is often limited…
Extras afișat conform termenilor RSS ai publicației · 78 cuvinte · textul integral și figurile sunt pe site-ul publicației
Subiecte conexe

News
Quantum Computing Report · 25 iul. 2026
News Archive 2026
Quantum Computing Report · 25 iul. 2026

Digital States 2026: AI Moves From Experiment to Infrastructure
govtech.com · 24 sept. 2026

Infleqtion (INFQ) and the Neutral-Atom Bet Behind Its NYSE Listing
Quantum Zeitgeist · 23 aug. 2026