Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification
arXiv:2205.05333 · doi:10.1371/journal.pone.0287869
Abstract
In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k-nearest neighbors (k-NN) algorithm, of which several quantum variants have been proposed. Nevertheless, they have not been employed yet as a preliminary step of other QML models, whereas the classical counterpart has already proven successful. In this paper, we present (i) an implementation in Python of a QML pipeline for local classification, and (ii) its extensive empirical evaluation. Specifically, the quantum pipeline, developed using Qiskit, consists of a quantum k-NN and a quantum binary classifier. The results have shown the quantum pipeline's equivalence (in terms of accuracy) to its classical counterpart in the ideal case, the validity of locality's application to the QML realm, but also the strong sensitivity of the chosen quantum k-NN to probability fluctuations and the better performance of classical baseline methods like the random forest.
33 pages, 8 figures, 9 tables
References in corpus (7)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- An introduction to quantum machine learning
- Quantum random access memory
- Quantum-enhanced machine learning
- Experimental Realization of Quantum Artificial Intelligence
- Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
- Quantum K-nearest neighbor classification algorithm based on Hamming distance