andretti autosport.

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.

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 will seek a competitive edge by piloting advanced analytics, machine learning and quantum-inspired applications. As quantum hardware matures, the team can test out the performance of new devices and algorithms on Orquestra, and easily swap in whatever backend delivers the best performance.
Our work together

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 will be in pole position to capture a competitive advantage.

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Exploring Use Cases in Machine Learning and Optimization

Tire Degradation Analysis

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.

Fuel Savings Optimization

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.

Predictive Modeling: Yellow Flag

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.

“INDYCAR racing is all about finding every possible edge and then maximizing it. Zapata’s expertise gives us that advantage through their Orquestra quantum software platform and expert scientists and engineers.”

Michael Andretti
CEO and Chairman, Andretti Autosport

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