Author

Andreas K. Maier

3 papers indexed here

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#machine learning Open access May 2026

Learning Logical Operations for Arbitrary Quantum Error Correction Codes

A general learning-based framework that, given only an encoding circuit, constructs physical implementations of logical operations while enforcing structural properties such as transversality or shallow depth, and position VarEFTQC as a proof-of-concept framework for discovering hardware-adapted logical gadgets for ear...

Nico Meyer, Christopher Mutschler, Dominik Seuss et al. · Physical Review A · 3 citations

#machine learning Apr 2026

Learning to Concatenate Quantum Codes

This work uses learning-based methods to tailor small, non-additive encoders when the noise exhibits sufficient structure, then switch to standard codes once the noise is nearly uniform, and achieves a target logical error rate with far fewer qubits than concatenating stabilizer codes alone.

Nico Meyer, Christopher Mutschler, Dominik Seuss et al. · arXiv.org · 3 citations

#machine learning Open access Jun 2025

Learning encodings by maximizing state distinguishability: variational quantum error correction

This work proposes a novel objective function for tailoring error correction codes to specific noise structures by maximizing the distinguishability between quantum states after a noise channel, ensuring efficient recovery operations.

Nico Meyer, Christopher Mutschler, Andreas K. Maier et al. · Quantum Science and Technolo... · 6 citations