Q-SAT-GEN - Hybrid generative modelling for satellite image denoising and infilling

卫星数据为世界各国政府和企业在科学、社会经济管理和商业应用方面提供了机会。对于越来越多依赖地球观测数据的组织和决策者来说，获取及时、可靠和可操作的信息越来越重要。由于传感器处理误差、云或大气扰动等噪声源、典型卫星图像的低空间分辨率以及重访时间在几小时到几天的数量级，从卫星图像中获取有用的见解可能会变得困难。缺失像素信息中的空洞或缺口会带来不确定性，并影响利益相关者的决策能力，这些利益相关者依赖准确的信息进行近实时的监控和推理。传统上，经典的生成式人工智能方法，如GANS或扩散模型，一直被用来解决这些问题。然而，这些方法需要大量的计算资源，并且存在广域网络中的模式崩溃等问题。ORCA计算将提供一种用于卫星图像处理的量子/经典混合生成算法，以减少训练模型所需的计算资源，并提高其性能。该算法使用ORCA的PT系列量子处理器和独特的软件堆栈。这一解决方案将有利于气候和天气监测、国防、环境和农业等领域的卫星监测目的。

Satellite data provides opportunities for governments and businesses around the world in scientific, socio-economic management and commercial applications. Access to timely, reliable, and actionable information is increasingly critical to a growing number of organizations and decision makers who rely on earth observation data.Gaining useful insights from satellite images can be difficult due to sources of noise such as sensor processing errors, clouds or atmospheric perturbations, the low spatial resolution of typical satellite images, and revisit times on the order of a few hours to days. Holes or gaps in of missing pixel information introduces uncertainty and affects decision making capabilities of stakeholders who rely on accurate information for near-real-time monitoring and inference purposes.Traditionally, classical generative AI methods such as GANs or diffusion models have been used to address these issues. However, these methods require significant computational resources and suffer from issues such as mode collapse in GANs.ORCA Computing will deliver a hybrid quantum/classical generative algorithm for satellite image processing both to reduce the computational resources required to train models and to improve their performance. This algorithm uses ORCA's PT-Series quantum processor and unique software stack. This solution will be beneficial for satellite monitoring purposes in areas such as climate and weather monitoring, defence, the environment and agriculture.