6,715 research outputs found
A review of k-NN algorithm based on classical and Quantum Machine Learning
[EN] Artificial intelligence algorithms, developed for traditional
computing, based on Von Neumann’s architecture, are slow and expen-
sive in terms of computational resources. Quantum mechanics has opened
up a new world of possibilities within this field, since, thanks to the basic
properties of a quantum computer, a great degree of parallelism can be
achieved in the execution of the quantum version of machine learning
algorithms. In this paper, a study has been carried out on these proper-
ties and on the design of their quantum computing versions. More specif-
ically, the study has been focused on the quantum version of the k-NN
algorithm that allows to understand the fundamentals when transcribing
classical machine learning algorithms into its quantum versions
Advances in quantum machine learning
Here we discuss advances in the field of quantum machine learning. The
following document offers a hybrid discussion; both reviewing the field as it
is currently, and suggesting directions for further research. We include both
algorithms and experimental implementations in the discussion. The field's
outlook is generally positive, showing significant promise. However, we believe
there are appreciable hurdles to overcome before one can claim that it is a
primary application of quantum computation.Comment: 38 pages, 17 Figure
Advantages of versatile neural-network decoding for topological codes
Finding optimal correction of errors in generic stabilizer codes is a
computationally hard problem, even for simple noise models. While this task can
be simplified for codes with some structure, such as topological stabilizer
codes, developing good and efficient decoders still remains a challenge. In our
work, we systematically study a very versatile class of decoders based on
feedforward neural networks. To demonstrate adaptability, we apply neural
decoders to the triangular color and toric codes under various noise models
with realistic features, such as spatially-correlated errors. We report that
neural decoders provide significant improvement over leading efficient decoders
in terms of the error-correction threshold. Using neural networks simplifies
the process of designing well-performing decoders, and does not require prior
knowledge of the underlying noise model.Comment: 11 pages, 6 figures, 2 table
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