TITLE:
Empirical Analysis of a Quantum Classifier Implemented on IBM’s 5Q Quantum Computer
AUTHORS:
Wei Hu
KEYWORDS:
Quantum Computation, Quantum Machine Learning, Quantum Circuit
JOURNAL NAME:
Journal of Quantum Information Science,
Vol.8 No.1,
March
7,
2018
ABSTRACT: The development of artificial
intelligence today is marked with increased computational power, new
algorithms, and big data. One such milestone impressive achievement in this
area is Google’s AlphaGo. However, this advancement is beginning to face increasing
challenges and the major bottleneck of AI today is the lack of adequate
computing power in the processing of big data. Quantum computing offers a new
and viable solution to deal with these challenges. A recent work designed a
quantum classifier that runs on IBM’s five qubit quantum computer and tested
its performance on the Iris data set as well as a circles data set. As quantum machine learning is still an emerging
discipline, it may be enlightening to conduct an empirical analysis of this
quantum classifier on some artificial datasets to help learn its unique
features and potentials. Our work on the quantum classifier can be summarized
in three parts. The first is to run its original version as a binary classifier
on some artificial datasets using visualization to reveal the quantum nature of
this algorithm, and the second is to analyze the swap operation utilized in its
original circuit due to the hardware constraint and investigate its impact on
the performance of the classifier. The last part is to extend the original
circuit for binary classification to a circuit for multiclass classification and
test its performance. Our findings shed new light on how this quantum
classifier works.