Optimizing continuous-time quantum error correction for Markovian and non-Markovian noise models
A new machine learning protocol is proposed that jointly optimizes the quantum error-correcting code space and the corresponding recovery map for continuous-time quantum error correction. It is designed to handle noise processes that may be correlated across both space and time. The abstract states that for a given Hilbert space and noise process, the protocol identifies an optimal code space and recovery map.
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What this could mean
- 0–2 yearsPlausible
Within two years, this protocol could be applied to noise models from specific quantum hardware platforms to automatically generate continuous-time error-correcting codes and recovery maps tailored to correlated noise, potentially outperforming manually designed codes.
The protocol already accepts arbitrary noise processes including spatial and temporal correlations, and machine learning optimization of code space and recovery map can be executed on classical simulations of noise from superconducting or trapped-ion systems. The main precondition is demonstrating computational scalability to relevant qubit numbers, which is plausible with current classical ML infrastructure.
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