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Research questionHow can quantum error-correcting encodings adapt to device-specific noise with less overhead?Near-term quantum devices have limited resources, while decoherence varies across hardware. A code effective under one noise channel may impose unnecessary overhead or preserve information poorly under another.
Machine Learning
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Latest papersRecent research connected to this question, newest first.Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error CorrectionThe source concerns variational quantum error correction, which learns encoding circuits by maximizing state distinguishability after a specified noise channel. It reports theoretical and practical properties, comparisons with standard codes, and proof-of-concept demonstrations on IBM and IQM hardware; these demonstrations do not establish broad deployment performance.research paper · Sep 2, 2026
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How can we select quantum error-correction codes across concatenation levels as the effective noise changes?How can machine-learning decoders for quantum error correction generalize to unseen codes while providing calibrated uncertainty?How can quantum neural networks prevent simulator-era postprocessing from silently discarding hardware measurements?How can synthetic-noise benchmarks reliably predict decoder rankings on real quantum hardware?
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