Quantum unsupervised and supervised learning on superconducting processors

Abhijat Sarma, Rupak Chatterjee, Kaitlin Gili, Ting Yu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm’s accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement for large datasets as compared to its classical counterpart.

Original languageEnglish
Pages (from-to)541-552
Number of pages12
JournalQuantum Information and Computation
Volume20
Issue number7-8
StatePublished - 2020

Keywords

  • IBMQ
  • K-Means Clustering
  • Quantum Machine Learning

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