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QI: Fachverband Quanteninformation
QI 7: Quantum Error Correction
QI 7.1: Vortrag
Montag, 18. März 2024, 15:00–15:15, HFT-TA 441
Outperforming Gottesman-Kitaev-Preskill quantum error correction via feedback with memory — •Matteo Puviani1, Sangkha Borah1,2, Remmy Zen1, Jan Olle1, and Florian Marquardt1,2 — 1Max Planck Institute for the Science of Light, 91058 Erlangen, Germany — 2Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of an harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctable well beyond the break-even point of the best passive encoding in the same system. However, the current approaches to quantum error correction (QEC) are based on protocols that only implement immediate measurement-based feedback. In our work, we train a recurrent neural network using the recently proposed Feedback GRAPE (Gradient Ascent Pulse Engineering with Feedback) method to develop a time-dependent QEC scheme based on feedback memory that outperforms current strategies and paves the way for novel measurement-based QEC.
Keywords: quantum error correction; bosonic code; GKP; machine learning; feedback-GRAPE