The Quantum Leap

Vikas Mishra

Quantum theory is rapidly evolving in various advanced technologies with impact to space applications. We are at the beginning of a paradigm shift, which is largely impacting Earth Observation (EO). An increasing amount of research in communications, optics, physics, nanotechnologies, large scale Artificial Intelligence (AI) innovations like quantum computing, generative AI and self-supervised learning (SSL) techniques etc provide fantastic opportunities to implement disruptive data analytics methods or instruments for space applications.

Exercise Vayu Shakti-24Quantum technology is already present in space, e.g., quantum key distribution. Latest investigation results validate the feasibility of actual applications and many other resources with unprecedented potentials such as quantum simulations, computing, imaging, sensing, metrology, optimisation or machine learning are at the edge of maturity. The open or easy access to early quantum computers represents a huge potential to discover new solutions and broaden the applications of EO.

Quantum computing can solve challenges of the Big EO Data and AI solutions. BigGIS refers to the integration of large-scale geographic information systems with big data technologies to manage and analyse vast amounts of geospatial data. This includes data from numerous Earth observation satellites and close range collection techniques such as LiDAR and mobile devices. As data collection outpaces consumption, integrating AI techniques for BigGIS becomes critical to effectively tackle the challenges it presents. Innovations such as SSL, quantum computing (QC) and generative AI enhance data processing and interpretation while providing new strategies for domain adaptation and transfer learning.

BigGIS

Remote sensing has evolved to become the primary technology supporting environmental monitoring, urban planning, agriculture, and defence in the age of big data. The retrieval of data, efficiently done through satellites, drones and other remote platforms, provides insights into a wealth of information about the surface and atmosphere of the earth. It can quickly provide static or dynamic geospatial data with varied scales and resolutions. The amount of data generated by aerial platforms can run into massive quantities, but very often, in dealing with geospatial data, storage and real-time processing remain challenging. It is the need of the hour to extract valuable information from these remote sensing data using computationally efficient techniques. This is where quantum computing, with its unparalleled computational power, offers promising solutions. BigGIS refers to the integration of largescale GIS with big evolving niche data technologies like generative AI, quantum computing etc to manage and analyse vast amounts of geospatial data generated by aerial or mobile platforms.

Although machine learning applications have demonstrated better accuracy than those using traditional algorithms or classifiers. There are several issues that hinder breakthroughs in remote sensing data classification viz complexity of algorithms and huge volume of remote sensing geospatial data and associated difficulties in distingu