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Abstract In recent years, multiple low light image enhancement algorithms have been developed, using both conventional techniques and machine learning based approaches. These techniques work as post processing methods, which add additional computational overhead. In this paper, a quantum image representation model has been proposed for low light images based on the Improved Simple Quantum Representation (ISQR). It is inspired by the Simple Quantum Representation (SQR) algorithm, which was originally designed for infrared imaging. ISQR extends its applicability by efficiently encoding low light grayscale and color images on quantum computing platforms As quantum imaging hardware is not yet commercially available, all experiments in this study were conducted through simulations to validate the proposed approach. ISQR is proposed as a quantum image representation technique in which classical pixel values are encoded into a quantum state , and an enhanced image is obtained after multiple quantum measurement and classical reconstruction. The enhancement performance of the proposed model is compared through multiple classical low light image enhancement algorithms and statistical analysis. In this paper, the coal mine and surveillance examples are shown primarily as qualitative case studies to demonstrate how ISQR behaves in extremely low light conditions. All these analyses and advantages highlight its potential for future quantum camera development.