  The detection and analysis of the solar coronal holes (CHs) is an important
field of study in the domain of solar physics. Mainly, it is required for the
proper prediction of the geomagnetic storms which directly or indirectly affect
various space and ground-based systems. For the detection of CHs till date, the
solar scientist depends on manual hand-drawn approaches. However, with the
advancement of image processing technologies, some automated image segmentation
methods have been used for the detection of CHs. In-spite of this, fast and
accurate detection of CHs are till a major issues. Here in this work, a novel
quantum computing-based fast fuzzy c-mean technique has been developed for fast
detection of the CHs region. The task has been carried out in two stages, in
first stage the solar image has been segmented using a quantum computing based
fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted
out from the segmented image based on image morphological operation. In the
work, quantum computing has been used to optimize the cost function of the fast
fuzzy c-mean (FFCM) algorithm, where quantum approximate optimization algorithm
(QAOA) has been used to optimize the quadratic part of the cost function. The
proposed method has been tested for 193 \AA{} SDO/AIA full-disk solar image
datasets and has been compared with the existing techniques. The outcome shows
the comparable performance of the proposed method with the existing one within
a very lesser time.
