  In this paper we have introduced a novel method for gamma hadron separation
in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine
Learning. IACTs captures images of Extensive Air Showers (EAS) produced from
very high energy gamma rays. We have used the QML Algorithms, Quantum Support
Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) for binary
classification of the events into signals (Gamma) and background(hadron) using
the image parameters. MAGIC Gamma Telescope dataset is used for this study
which was generated from Monte Carlo Software Coriska. These quantum algorithms
achieve performance comparable to standard multivariate classification
techniques and can be used to solve variety of real-world problems. The
classification accuracy is improved by hyper parameter tuning. We propose a new
architecture for using QSVC efficiently on large datasets and found that
clustering enhance the overall performance.
