2,574 research outputs found
Scalable Neural Network Decoders for Higher Dimensional Quantum Codes
Machine learning has the potential to become an important tool in quantum
error correction as it allows the decoder to adapt to the error distribution of
a quantum chip. An additional motivation for using neural networks is the fact
that they can be evaluated by dedicated hardware which is very fast and
consumes little power. Machine learning has been previously applied to decode
the surface code. However, these approaches are not scalable as the training
has to be redone for every system size which becomes increasingly difficult. In
this work the existence of local decoders for higher dimensional codes leads us
to use a low-depth convolutional neural network to locally assign a likelihood
of error on each qubit. For noiseless syndrome measurements, numerical
simulations show that the decoder has a threshold of around when
applied to the 4D toric code. When the syndrome measurements are noisy, the
decoder performs better for larger code sizes when the error probability is
low. We also give theoretical and numerical analysis to show how a
convolutional neural network is different from the 1-nearest neighbor
algorithm, which is a baseline machine learning method
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches.Comment: Accepted to SIGIR 201
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