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Trapped Ion Quantum Computing
Quantum Machine Learning
Pattern capacity of a single quantum perceptron
arXiv
Authors: Fabio Benatti, Giovanni Gramegna, Stefano Mancini
Year
2021
Paper ID
40487
Status
Preprint
Abstract Read
~2 min
Abstract Words
71
Citations
N/A
Abstract
Recent developments in Quantum Machine Learning have seen the introduction of several models to generalize the classical perceptron to the quantum regime. The capabilities of these quantum models need to be determined precisely in order to establish if a quantum advantage is achievable. Here we use a statistical physics approach to compute the pattern capacity of a particular model of quantum perceptron realized by means of a continuous variable quantum system.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2021 reference point for readers tracking recent quantum research.
- Recent developments in Quantum Machine Learning have seen the introduction of several models to generalize the classical perceptron to the quantum regime.
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