Design enables disruptive AI drone speed, precision for future commercial and US Defense
ZenaTech, Inc. , Vancouver-based specialists in AI drones and solutions, has successfully developed its first quantum computing prototype consisting of a framework for the rapid analysis and processing of large datasets for its AI drone solutions. Using weather forecasting algorithms as part of its Clear Sky project as a test case, the company has created a precedent framework for real time analysis of massive amounts of data that can be captured through AI drone sensors while in the air.
The firm envisions commercial applications ranging from highly efficient precision agriculture to predictive energy infrastructure inspections. Defense applications include enhancing real-time battlefield decision-making with faster and more precise threat detection, reconnaissance, and advance electronic warfare capabilities.
Source: ZenaTech Inc.
“We’re not just building smarter drones, we’re building a quantum-intelligent edge where data becomes decisions in an instant, whether it’s a battlefield or a farm field,” said Shaun Passley, Ph.D., ZenaTech CEO. “We believe this quantum framework we are creating is just the beginning as we’ve now demonstrated it can use it for large datasets. We plan to keep expanding R&D capabilities, with the goal of growing our team of 6 to 25 over the coming months. The end goal is clear: accelerate time to market, reduce operational costs, and lead the industry as a true innovator,” added Dr. Passley.
ZenaTech’s Clear Sky project is one of the company’s quantum computing R&D initiatives focused on weather forecasting that will use AI drones and drone fleets plus quantum to better predict localized weather for more accurate prediction of extreme weather events saving lives and reducing costs and destruction. The weather application and algorithms used for the prototype track and analyze multiple key atmospheric parameters such as temperature, humidity, wind, barometric pressure, and precipitation. Internal testing using historical open-source data has shown a high degree of accuracy with trusted weather platforms and actual data, validating both its accuracy and reliability.