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Automotive

Rising fuel costs, congested roads and increased demand for just-in-time delivery create huge pressures. Quantum computing will redefine mobility, logistics, materials, and more, transforming automotive.

  • Shape the Future of Mobility

    Automotive companies are increasingly transitioning from being centered around the sale of a commodity product to the provision of mobility as a service. This creates a broad range of complex computational challenges. Players that effectively address this with quantum technologies are well placed to lead.

  • Improve Operations and Supply Chains

    Quantum technology can reduce the impact of supply chain disruptions and enable greater production capacity with existing resources.

  • Enhance Autonomous Driving

    Quantum machine learning can develop and train models that will improve prediction accuracy, improve sensors and better deal with complex, real-world situations.

Automotive companies are increasingly transitioning from being centered around the sale of a commodity product to the provision of mobility as a service. This creates a broad range of complex computational challenges. Players that effectively address this with quantum technologies are well placed to lead.

Quantum technology can reduce the impact of supply chain disruptions and enable greater production capacity with existing resources.

Quantum machine learning can develop and train models that will improve prediction accuracy, improve sensors and better deal with complex, real-world situations.

Discover high-impact results

Improve your logistics, invent new battery technologies and develop better assisted, piloted and automated driving. Additionally, improve the vehicle production process using quantum-automated parts and vehicle design.

Our impact
  • Process Optimization

    Optimizing operational processes, such as workflow scheduling at the assembly line.
  • Routing and Mobility Optimization

    Predicting traffic volume to optimize routing and mobility services.
  • Image Recognition

    Using quantum-enhanced machine learning to improve image recognition for car classification.
  • Design Quality and Efficiency

    Introducing innovative vehicle and battery design to improve quality and reduce CO2.
A running car covered with particles of light
Use Case: Image Recognition in the Automotive Industry

Together with the Volkswagen Group’s Data Lab, Terra Quantum aimed to improve the accuracy of image recognition. In the manufacturing context, these systems are highly valuable for fault detection, for example. In the future, such models could also make a valuable contribution to the performance of self-driving vehicles.

The team developed a new hybrid quantum machine learning algorithm and deployed it on the QMware hybrid quantum cloud. In scientific terms, they built a hybrid quantum residual neural network model, enhanced through quantum elements and a new approach for a quantum-inspired tensor train hyperparameter optimization.

The team benchmarked this method over classical machine learning approaches and observed performance improvements in the form of reduced expected run times and fitness as the problem size scales. The newly developed approach can provide increased accuracy in image recognition tasks in fewer iterations.

Picture of Dr. Florian Neukart

Dr . Florian Neukart

Chief Product Officer

In the mobility industry, quantum computing will help solve complex optimization problems, such as logistics or traffic flow, support the discovery of new materials and structures, such as new battery chemistry, and create an essential advantage for self-driving vehicles through hybrid machine learning. In the latter, the underlying software, comprising complex algorithms for environment perception, localization and mapping, and more, can be trained utilizing hybrid quantum computing before being deployed in the cars.

The critical performance of environment perception serves as a perfect application for quantum algorithms to prove its superiority over purely classical machine learning. What's more, reduced training times will lead to shorter innovation cycles.

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