TY - JOUR
T1 - Quantum unsupervised and supervised learning on superconducting processors
AU - Sarma, Abhijat
AU - Chatterjee, Rupak
AU - Gili, Kaitlin
AU - Yu, Ting
N1 - Publisher Copyright:
© Rinton Press.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - IBMQ
KW - K-Means Clustering
KW - Quantum Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85086316853&partnerID=8YFLogxK
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M3 - Article
AN - SCOPUS:85086316853
SN - 1533-7146
VL - 20
SP - 541
EP - 552
JO - Quantum Information and Computation
JF - Quantum Information and Computation
IS - 7-8
ER -