Machine Learning in the Quantum Age: Quantum vs. Classical Support
  Vector Machines
Davut Emre Tasar Kutan Koruyan Ceren Ocal Tasar
This work endeavors to juxtapose the efficacy of machine learning algorithms
within classical and quantum computational paradigms. Particularly, by
emphasizing on Support Vector Machines (SVM), we scrutinize the classification
prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational
on quantum hardware over the Iris dataset. The methodology embraced
encapsulates an extensive array of experiments orchestrated through the Qiskit
library, alongside hyperparameter optimization. The findings unveil that in
particular scenarios, QSVMs extend a level of accuracy that can vie with
classical SVMs, albeit the execution times are presently protracted. Moreover,
we underscore that augmenting quantum computational capacity and the magnitude
of parallelism can markedly ameliorate the performance of quantum machine
learning algorithms. This inquiry furnishes invaluable insights regarding the
extant scenario and future potentiality of machine learning applications in the
quantum epoch. Colab: https://t.ly/QKuz0