The Challenge
Races generate a lot of data — about 1 terabyte per car. Andretti Autosport is always looking for better ways to analyze that data to gain an edge over the competition. Their goal? Upgrade their existing data analytics infrastructure to be quantum-ready, driving their race strategy with the latest machine learning and quantum techniques.
Our Approach
In 2022, Zapata began upgrading Andretti’s analytics infrastructure to be quantum-ready. Together, we sought a competitive edge by piloting advanced analytics, machine learning and quantum-inspired applications.
These applications addressed three critical racing challenges, which have analogous use cases across industries. Predicting tire degradation to optimize the timing of tire changes, for example, translates to predictive maintenance in manufacturing, logistics, and other industries. Optimizing fuel consumption to reduce refueling frequency could be applied to help enterprises reduce their carbon footprint or improve delivery times. And the same methods for forecasting yellow flag conditions to enable preemptive pit stops could help enterprises across industries better anticipate and respond to disruptive events.
As quantum hardware matures, the team could test out the performance of new devices and algorithms on Zapata’s quantum software platform, and swap in the backend that delivers the best performance.
