公开(公告)号：US12057859B1

公开(公告)日：2024-08-06

申请号：US17937411

申请日：2022-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Christopher Chamberland ,  Luis Goncalves ,  Prasahnt Sivarajah ,  Eric Christopher Peterson ,  Sebastian Johannes Grimberg

IPC: H03M13/15 ,  G06N10/60 ,  G06N10/70 ,  H03M13/00

CPC classification number: H03M13/159 ,  G06N10/60 ,  G06N10/70 ,  H03M13/611

Abstract: Techniques for implementing a local neural network 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. A local neural network decoder may be pre-trained via a supervised learning technique such that the local neural network decoder may be applied for error correction in the presence of circuit-level noise in arbitrarily sized surface codes in a local decoding stage. Prior to a global decoding stage, an intermediate stage may be used to remove vertical pairs of highlighted vertices within the matching graph, which may reduce a syndrome density within the matching graph to allow for faster decoding at the global decoding stage. Such an intermediate stage may include application of a syndrome collapse or vertical cleanup technique.

公开(公告)号：US12165013B1

公开(公告)日：2024-12-10

申请号：US17937413

申请日：2022-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Christopher Chamberland ,  Luis Goncalves ,  Prasahnt Sivarajah ,  Eric Christopher Peterson ,  Sebastian Johannes Grimberg

IPC: G06F11/00 ,  G06F11/08 ,  G06F11/10 ,  G06N3/04 ,  G06N10/20 ,  G06N10/70

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.