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A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal Holes

Sanmoy Bandyopadhyay, Suman Kundu

TL;DR

This paper targets real-time detection of solar coronal holes by coupling a histogram-based fast fuzzy c-means clustering with a quantum-optimized center-finding step using a three-block ADMM and QAOA. The two-stage QCFFCM framework is followed by image morphological operations to extract CH regions, reducing computation while maintaining boundary fidelity. Experimental results on 2017 SDO/AIA 193 Å data show QCFFCM achieves competitive accuracy and F1 scores compared to established methods, with a near real-time runtime (~12 seconds per image) on modest hardware. The work demonstrates the practical potential of quantum-inspired optimization to accelerate solar image analysis for space weather forecasting, while also noting sensitivity to area-thresholding and ground-truth variances.

Abstract

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 Å 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.

A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal Holes

TL;DR

This paper targets real-time detection of solar coronal holes by coupling a histogram-based fast fuzzy c-means clustering with a quantum-optimized center-finding step using a three-block ADMM and QAOA. The two-stage QCFFCM framework is followed by image morphological operations to extract CH regions, reducing computation while maintaining boundary fidelity. Experimental results on 2017 SDO/AIA 193 Å data show QCFFCM achieves competitive accuracy and F1 scores compared to established methods, with a near real-time runtime (~12 seconds per image) on modest hardware. The work demonstrates the practical potential of quantum-inspired optimization to accelerate solar image analysis for space weather forecasting, while also noting sensitivity to area-thresholding and ground-truth variances.

Abstract

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 Å 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.
Paper Structure (8 sections, 20 equations, 6 figures, 3 tables)

This paper contains 8 sections, 20 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Work
  4. Results and Discussions
  5. Visual Analysis
  6. Quantitative Analysis
  7. Execution Time Analysis
  8. Conclusion

Figures (6)

  • Figure 1: Block-diagram of the proposed FFCM-SCM based coronal holes detection in solar disk image.
  • Figure 2: A pictorial summarization showing (i) Original Image followed by ground-truth images in (ii) and (iii), and result obtained using model like, CNN Seg, SPoCA, CHIMERA, ACWE, FFCM-SCM, POR-ACM, FEDCM and the proposed method in (iv)-(xi) respectively.
  • Figure 3: CHs area detected for the year 2017, using existing methods and proposed method QFFCM.
  • Figure 4: Skewness value detected for a carrington rotation in the year 2017, using existing methods and proposed method QFFCM.
  • Figure 5: A pictorial summary depicting the confusion matrix generated for results obtained using models such as CNN Seg, SPoCA, CHIMERA, ACWE, FFCM-SCM, POR-ACM, FEDCM, and the proposed method in (i)-(viii) respectively, when compared with the SM-GT.
  • ...and 1 more figures