Quantum One-class Classification With a Distance-based Classifier
This addresses the challenge of implementing quantum algorithms on noisy, limited-qubit hardware for researchers in quantum computing, though it appears incremental as it builds on an existing classifier.
The authors tackled the problem of quantum hardware limitations by developing a minimal quantum machine learning model called Quantum One-class Classifier (QOCC), which reduces qubits and operations to mitigate errors in NISQ computers, and experimental results show it outperforms the Hadamard Classifier.
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.