Training neural network local decoders for circuit-level quantum error correction
US12165013B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2022 |
| Grant date | Dec 10, 2024 |
| Priority date | — |
| Expiry date | Apr 14, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH03M13/611
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for training local decoders for use in a local and global decoding scheme for quantum error correction of circuit-level noise within quantum surface codes such that the decoding schemes have fast decoding throughout and low latency times for quantum algorithms are disclosed. The local decoders may have a neural network architecture and may be trained using training data sets comprising simulated rounds of syndrome measurements for respective simulated quantum surface codes in addition to information such as syndrome differences, qubit placements, and temporal boundaries within the simulated rounds of syndrome measurements in order to train the local decoders for arbitrarily sized quantum surface codes and arbitrary numbers of rounds of syndrome measurements. Following a local decoding stage in which a large number of data errors have been corrected by a local decoder, error correction for remaining errors may continue with a more efficient global decoding stage.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.