Quantum Discriminator for Binary Classification

• URL: http://arxiv.org/abs/2009.01235v3
• Date: Fri, 28 Jan 2022 02:36:43 GMT
• Title: Quantum Discriminator for Binary Classification
• Authors: Prasanna Date and Wyatt Smith
• Abstract summary: We propose a novel quantum machine learning model called the Quantum Discriminator. We show that the quantum discriminator can attain 99% accuracy in simulation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in O(N logN) time, and inferencing is performed on a universal quantum computer in linear time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit initialized to the zero state and outputs the predicted label. We analyze its performance on the Iris data set and show that the quantum discriminator can attain 99% accuracy in simulation.

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