Quantum Binary Classification (Student Abstract)

Authors

  • Carla Silva Faculty of Sciences, University of Porto
  • Ana Aguiar Instituto de Telecomunicações Faculty of Engineering, University of Porto
  • Inês Dutra CINTESIS Faculty of Sciences, University of Porto

Keywords:

Quantum Machine Learning, Quantum Supervised Binary Classification, AI Applications

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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Published

2021-05-18

How to Cite

Silva, C., Aguiar, A., & Dutra, I. (2021). Quantum Binary Classification (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15889-15890. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17941

Issue

Section

AAAI Student Abstract and Poster Program