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Quantum Machine Learning
Quantum Simulation
Polyadic Quantum Classifier
arXiv
Authors: William Cappelletti, Rebecca Erbanni, Joaquín Keller
Year
2020
Paper ID
21914
Status
Preprint
Abstract Read
~2 min
Abstract Words
99
Citations
N/A
Abstract
We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy --compared to a classical machine learning model-- for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures.
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