Quantum Binary Classification (Student Abstract)

AAAI 2021 pp. 15889-15890

doi:10.1609/AAAI.V35I18.17941 /aaai/2021/silva2021aaai-quantum/

Abstract

We implement a quantum binary classifier where given a dataset of pairs of training inputs and target outputs our goal is to predict the output of a new input. The script is based in a hybrid scheme inspired in an existing PennyLane's variational classifier and to encode the classical data we resort to PennyLane's amplitude encoding embedding template. We use the quantum binary classifier applied to the well known Iris dataset and to a car traffic dataset. Our results show that the quantum approach is capable of performing the task using as few as 2 qubits. Accuracies are similar to other quantum machine learning research studies, and as good as the ones produced by classical classifiers.

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Cite

Text

Silva et al. "Quantum Binary Classification (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17941

Markdown

[Silva et al. "Quantum Binary Classification (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/silva2021aaai-quantum/) doi:10.1609/AAAI.V35I18.17941

BibTeX

@inproceedings{silva2021aaai-quantum,
  title     = {{Quantum Binary Classification (Student Abstract)}},
  author    = {Silva, Carla and Aguiar, Ana and Dutra, Inês},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15889-15890},
  doi       = {10.1609/AAAI.V35I18.17941},
  url       = {https://mlanthology.org/aaai/2021/silva2021aaai-quantum/}
}