Just five races into the NTT INDYCAR SERIES season, Andretti Autosport and Zapata Computing have identified valuable use cases regarding the use of accelerated analytic capabilities to realize a real-time performance edge on race day.
Engineers, data scientists and race strategists from both organizations are working side-by-side and using Zapata’s Orquestra platform for building and deploying quantum-ready applications in the Zapata Computing | Andretti Autosport Race Analytics Command Center (R.A.C.C.) before, during and after each race.
The results to date have identified new approaches that can help generate predictive insights, measure and optimize performance and inform race strategy. Specifically, the teams are capitalizing on critical Andretti Autosport data sets to build advanced ML models to better understand tire degradation analysis, identify fuel savings opportunities and improve yellow flag predictive modeling.
To date the team has discovered several analytical strategies that could give Andretti Autosport a competitive edge in the future. Along with Andretti’s engineering team, Zapata engineers are constructing a roadmap of additional use cases to explore, including:
- Deploying a hybrid infrastructure that leverages both cloud and edge computing capabilities. Orquestra helps this initiative because deployment is natively hybrid multi-cloud and on premise/edge.
- Developing enhanced visualization capabilities to accelerate decision making on the racetrack while monitoring model performance and adding model interpretability.
- Exploring acceleration and enhancement of optimization tasks and time series forecasting via quantum-inspired and quantum-enabled algorithms.
Eric Bretzman, Technical Director at Andretti Autosport
Each year we collect and process more information than the last. Modernizing our infrastructure will add efficiency and power. There are critical new challenges on our job list today that we are fortunate to share with Zapata Computing to set the direction of our future engineering infrastructure.
Yudong Cao, CTO of Zapata Computing
The insights hidden in those data points are invaluable when it comes to squeezing out every ounce of performance possible on the racetrack. Most of what we are using today to ‘connect the dots’ is driven by advanced machine learning on classical computers, but we’re already seeing some tremendous opportunities to implement quantum-inspired methods on classical hardware—and then use those methods on quantum hardware backends as they mature on their path to being useful in enterprise production environments.
