for Aerospace & Automotive
AI, quantum techniques and other Big ComputeTM promise to unlock new advances in fluid dynamics, autonomous driving, supply chain optimization and traffic management. According to McKinsey, even a 2-5% increase in productivity would create $10 billion to $25 billion in value for the industry each year.
Disruptions are common in the transportation industry, particularly among airlines. Since large logistics networks are closely interconnected — one plane may fly multiple routes in a single day — a small disruption in one place can lead to costly losses across the entire network. In fact, irregular operations (IROPs) can cost airlines as much as 8% of their revenue. Airline operators are often forced to weigh multiple competing considerations when rescheduling flights such as time, costs, further delays, and passenger compensation. The result is a complex multi-factor combinatorial optimization problem that needs to be solved in real-time.
Complex combinatorial optimization problems are typically difficult to solve on classical computers, even approximately. However, our Generator-Enhanced Optimization (GEO) approach has a demonstrated ability to provide an advantage for complex optimization problems. It does so by using quantum or quantum-inspired generative machine learning models to learn from and improve upon the results generated by best-in-class classical solvers. In doing so, it can support the real-time optimization and re-routing of planes and other logistics networks while accounting for numerous parameters.Learn more about GEO
Applications for Aerospace and Automotive
Computational Fluid Dynamics (CFD) Simulation
Speed up data analysis, improve accuracy and reduce costs using quantum CFD simulations for aerodynamic design.
Heat Transfer Inverse Design
Improve heat transfer in designs for engines and other vehicle components using generator-enhanced optimization (GEO) with PDE constraints.
Identify failure modes through quantum-enhanced fault-tree analysis to improve safety in future vehicle designs.
Design Process Optimization
Balance various product functionalities with safety, reliability and costs in design process optimization models and solve them with GEO.
Manufacturing Process Optimization
Increase efficiency by optimizing the design of factories, employee schedules, and machine processes using quantum or quantum-inspired prescriptive analytics.
Improve object recognition in autonomous vehicles using quantum-enhanced supervised learning.
Supply Chain Optimization
Optimize the location of facilities and selection of suppliers, distributors, and vendors for product quality, costs, delivery times, and demand coverage using GEO.
Optimize the stocking of raw materials and manufacturing components to prevent overflows and reduce costs using quantum or quantum-inspired prescriptive analytics.
Optimize the speed and route of airplanes, buses, and other vehicles to minimize fuel consumption using GEO.
Predict maintenance needs for fleet vehicles using variational quantum classifier and other quantum-enhanced machine learning techniques.
Optimize the re-routing of planes and other logistics networks to react quickly to irregular operations using quantum-enhanced optimization techniques.
Network Planning Optimization
Optimize the scheduling and routing of fleet vehicles to account for demand, crew scheduling, fuel planning, and other concerns using GEO.
Use Case Timeline (est.)
At over 200mph, tires degrade quickly: a typical Indy 500 race includes around five tire changes per car. A key part of race strategy for teams like Andretti Autosport is knowing when to take a pit stop — a problem similar to predictive maintenance problems faced by automotive manufacturers.
Since spring 2022, we’ve worked with Andretti Autosport to upgrade their analytics infrastructure and build machine learning models for race strategy. This includes models for tire degradation analysis and fuel savings optimization that translate directly to automotive and OEM use cases
Orquestra® Benefits for Aerospace and Automotive
Orchestration Across Environments
Leverage the heterogenous compute resources best suited for your tasks, without getting locked into any one hardware platform. Deploy across hybrid backends at enterprise scale.
Data Management & Velocity
Store, retrieve, and analyze large datasets. Streamline data management from ingestion to export to accelerate data velocity.
Keep what works now. Integrate existing and future solutions with a framework optimized for extensibility, interoperability, and innovation.
Workflow Development and Deployment
One unified platform to go from research to development to deployment with extensible, scalable, modular workflows.
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