Volume 119, Number 6, September 2017
|Number of page(s)
|01 December 2017
Implementing a distance-based classifier with a quantum interference circuit
1 Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal Durban 4000, South Africa
2 Maastricht Science Programme, University of Maastricht - 6200 MD Maastricht, The Netherlands
3 National Institute for Theoretical Physics - KwaZulu-Natal, Durban 4000, South Africa
Received: 28 August 2017
Accepted: 3 November 2017
Lately, much attention has been given to quantum algorithms that solve pattern recognition tasks in machine learning. Many of these quantum machine learning algorithms try to implement classical models on large-scale universal quantum computers that have access to non-trivial subroutines such as Hamiltonian simulation, amplitude amplification and phase estimation. We approach the problem from the opposite direction and analyse a distance-based classifier that is realised by a simple quantum interference circuit. After state preparation, the circuit only consists of a Hadamard gate as well as two single-qubit measurements, and computes the distance between data points in quantum parallel. We demonstrate the proof of principle using the IBM Quantum Experience and analyse the performance of the classifier with numerical simulations.
PACS: 03.67.Ac – Quantum algorithms, protocols, and simulations / 03.67.Lx – Quantum computation architectures and implementations
© EPLA, 2017
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