Quantum-ready infrastructure, powered by Orquestra®
Andretti Autosport is upgrading their analytics infrastructure and building ML and quantum-inspired solutions to enhance decision making—and win more races.
Quantum-Ready Today, Quantum Tomorrow
The use cases we’re testing and piloting are classical machine learning solutions, using today’s data, compute, and architecture. The team is also testing quantum-inspired methods running on classical compute. But by running on Orquestra, these applications are forward-compatible with quatum and other exotic hardware — so that when it is ready, Andretti is in position to capture a competitive advantage.
In early 2022, we deployed the Orquestra® platform within the Andretti Autosport | Zapata Computing Race Analytics Command Center (RACC). The RACC is a mobile engineering environment where engineers from Zapata and Andretti work side-by-side in pursuit of a real-time advantage on race day. The hybrid infrastructure combines data lake integration, cloud and dashboards to drive decision-making — all managed with Orquestra. Engineers working from the RACC are testing various use cases in machine learning and optimization.
Tires wear out quickly going over 200MPH, requiring time-consuming pit stops to change tires. Zapata is working with Andretti to create a machine learning model that can guide strategic decisions around tire changes, such as when a car should swap out tires, which tires should be used, and how often they should change tires based on current conditions. This use case translates to predictive maintenance problems across industries.
The fewer times a car has to refuel, the more time it can save in the race. Zapata and Andretti are applying machine learning and advanced analytics to help drivers optimize their fuel consumption and determine the best timing for refueling. Similar fuel savings solutions have wide applicability in any industry looking to shrink its carbon footprint or time to delivery.
When there’s an accident or debris on the track, drivers are required to reduce their speed and are prohibited from passing other cars. This is a yellow flag, and since cars aren’t going full speed, it’s often a good time for a pit stop. Zapata and Andretti are creating a model to predict when a yellow flag is likely based on track conditions, the status of various cars, the drivers in those cars, and other factors. This ability to predict and preemptively respond to disruptive events has wide applicability beyond racing.
CEO and Chairman, Andretti Autosport