Qutrit Ternary Image Circuits for Geospatial GIS Lunar Surveying Utilizing a Novel Technique

This paper explores the integration of low-altitude CubeSats for signal telemetry while enhancing these CubeSats for the acquisition of imaging data. The objective of this study is to improve a CubeSat by incorporating a radar system, lidar, and a fish-eye lens camera to capture high-resolution imaging data from long-range signals. This process will employ a qutrit-based quantum optimization circuit to achieve superior image reconstruction. The innovative aspect of this approach lies in a unique reconstruction technique that leverages qutrit circuitry to enhance both the reconstruction and optimization processes. This research focuses on utilizing quantum computing to analyze geospatial imaging data obtained from a CubeSat equipped with a specialized compute module. Investigations into the processors within this computing framework have identified parallels with an existing patented signal processing method, which serves as a foundational model for the compute module. Since image construction is dependent on raw data and includes sonar, thermal, and various signal types in geospatial reconstruction, the application of qutrits can significantly improve both the speed and quality of the process. This proposed solution aims to strengthen lunar missions by enhancing imaging and surveying capabilities. The paper reviews the current literature on this innovative methodology and seeks to provide essential foundational support for future related experiments. Additionally, a qubit-based grid computing architecture will be employed for quantum optimization.