Learning encodings by maximizing state distinguishability: variational quantum error correction
Meyer N, Mutschler C, Maier A, Scherer DD (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Book Volume: 11
Article Number: 045036
Journal Issue: 4
Abstract
Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them impractical for near-term, early fault-tolerant devices. We propose 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. We formalize this concept with the distinguishability loss function, serving as a machine learning objective to discover resource-efficient encoding circuits optimized for given noise characteristics. We implement this methodology using variational techniques, termed variational quantum error correction. Our approach yields codes with desirable theoretical and practical properties and surpasses standard codes of comparable size under structured noise. We also provide proof-of-concept demonstrations on IBM and IQM hardware devices, highlighting the practical relevance of our procedure.
Authors with CRIS profile
Involved external institutions
Germany (DE)How to cite
APA:
Meyer, N., Mutschler, C., Maier, A., & Scherer, D.D. (2026). Learning encodings by maximizing state distinguishability: variational quantum error correction. Quantum Science and Technology, 11(4). https://doi.org/10.1088/2058-9565/ae98b1
MLA:
Meyer, Nico, et al. "Learning encodings by maximizing state distinguishability: variational quantum error correction." Quantum Science and Technology 11.4 (2026).
BibTeX: Download