Quantum One-class Classification With a Distance-based Classifier

TL;DR

This paper introduces a minimal quantum one-class classifier called QOCC, designed for noisy intermediate-scale quantum devices, demonstrating advantages over the Hadamard Classifier through experimental results.

Contribution

The paper proposes a new quantum one-class classifier that uses fewer qubits and operations, suitable for current noisy quantum hardware, and shows its effectiveness experimentally.

Findings

QOCC outperforms HC in experiments on quantum devices.

QOCC requires fewer qubits and operations, reducing hardware errors.

Experimental results validate the advantages of QOCC over HC.

Abstract

The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.