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Engineering & Technology

Quantum transforms travel

H. Vella

Published May 1, 2019• 6 citations
Publisher

Abstract

As cars become increasingly connected and aircraft ever more sensor-laden, automotive and aerospace companies are making a transition from being 'nuts and bolts' manufacturers to so-called `mobility companies' that collect and use unprecedented amounts of data. All information, however, requires processing at super-fast speeds if it's to be useful. To do this, companies are exploring not only AI and machine learning but also the more experimental realm of quantum computing, to see if it can extract better solutions faster than the classical equivalent. Leading the charge is Volkswagen AG. In November last year, the company announced it had, for the first time, used a quantum computer to develop a traffic management system that, it claims, will replace forecasts of urban traffic volumes, transport demand and travel times by precise calculations. Volkswagen is not alone in its quantum efforts. Nearly every automotive company is currently developing a quantum strategy, including Daimler, BMW, Ford and Toyota. In aerospace, Airbus launched its 'Airbus Quantum Computing Challenge' in January.

DOI
10.1049/et.2019.0404

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As cars become increasingly connected and aircraft ever more sensor-laden, automotive and aerospace companies are making a transition from being 'nuts and bolts' manufacturers to so-called `mobility companies' that collect and use unprecedented amounts of data. All information, however, requires processing at super-fast speeds if it's to be useful. To do this, companies are exploring not only AI and machine learning but also the more experimental realm of quantum computing, to see if it can extract better solutions faster than the classical equivalent. Leading the charge is Volkswagen AG. In November last year, the company announced it had, for the first time, used a quantum computer to develop a traffic management system that, it claims, will replace forecasts of urban traffic volumes, transport demand and travel times by precise calculations. Volkswagen is not alone in its quantum efforts. Nearly every automotive company is currently developing a quantum strategy, including Daimler, BMW, Ford and Toyota. In aerospace, Airbus launched its 'Airbus Quantum Computing Challenge' in January.
Integrating quantum computing and artificial intelligence (AI) in automotive safety presents a paradigm shift, addressing complex challenges such as real-time traffic management, accident prevention, and system optimization. Quantum computing's principles of superposition, entanglement, and parallelism enhance AI's ability to process and analyze vast datasets, enabling precise and efficient decision-making in dynamic environments. Industry leaders like BMW, Volkswagen, and Waymo demonstrate the transformative potential of quantum-enhanced systems with applications in traffic optimization, autonomous navigation, and predictive maintenance. However, hardware scalability, ethical concerns, and regulatory gaps persist.
By improving their computational capacity, sensory precision, and decision-making powers, quantum technologies have the potential to completely transform autonomous car systems. These systems can handle enormous volumes of data more effectively and make choices in real time with previously unheard-of precision by utilizing quantum computing, quantum communication, and quantum sensing. While quantum sensing technologies—such as quantum radar and quantum-enhanced lidar—improve object detection and identification in difficult environments, quantum computing holds the promise of optimizing complex algorithms for path planning, traffic management, and machine learning models. the incorporation of quantum technologies into self-driving car systems, investigating how these developments can help with today's perception, navigation, and safety issues. We also talk about the possibility for quantum-enhanced autonomous systems to hasten the creation of completely autonomous transportation networks and its ramifications for the future.
Q(AI)2\mathrm{Q(AI)}^2Q(AI)2: Quantum Artificial Intelligence for the Automotive Industry
The goal of the project Q(AI)2\mathrm{Q(AI)}^2Q(AI)2 was to acquire a broader basis of quantum computing enhanced AI and optimization algorithms for potential applications in the automotive industry. In particular, we strove to assess the potential for quantum advantage for specific use cases from the automotive industry. For this, we developed quantum algorithmic solutions tailored both to the available hardware as well as to the industrial problems at hand. In addition, we ensured industrial relevance of the investigated applications by the constitution of the consortium, involving all three large German car manufactures VW, Mercedes-Benz and BMW as well as Bosch as a central component supplier. Forschungszentrum Jülich and the German Center for Artificial Intelligence (DFKI) provided the necessary expertise in AI and quantum computing methods.
Quantum computing holds transformative potential for Intelligent Transportation Systems (ITS), offering solutions to complex and computationally intensive challenges in modern transportation networks. By leveraging quantum mechanics principles like superposition and entanglement, quantum algorithms can optimize traffic management, route planning, and logistics with unprecedented speed and accuracy. This can significantly enhance real-time traffic flow predictions, dynamic route optimization, and efficient resource allocation, particularly for electric vehicle networks, promoting sustainable and energy-efficient transportation solutions. However, integrating quantum computing into ITS faces challenges. Current quantum hardware is limited by issues like qubit instability, short coherence times, and high error rates, impeding large-scale applications. Additionally, developing hybrid systems that combine classical and quantum computing capabilities, and ensuring robust data privacy and security, are significant hurdles.
Companies like Intel, a pioneer in chip design, are driving the transition from classical computing to the next generation of quantum computing. Alongside Intel, tech giants such as IBM, Microsoft, and Google are also making significant strides in this domain. The race is on to create the world's first functional quantum computer capable of fulfilling the technology's long-promised potential—developing revolutionary materials, encrypting data with near-perfect security, and accurately forecasting climate changes. While such breakthroughs may still be over a decade away, companies like IBM, Microsoft, Google, Intel, and other leading players continue to celebrate even the smallest incremental advancements in this field. Most of these milestones revolve around increasing the number of quantum bits, or qubits—the foundational units of information in a quantum computer—on a processor chip. However, the journey to quantum computing extends far beyond controlling subatomic particles. Quantum computing's expanding capabilities are opening up new possibilities for processing the immense volumes of structured and unstructured data generated today. This progress serves as a powerful enhancement to artificial intelligence (AI), enabling it to evolve toward the next generation of super artificial intelligence (SAI) in the near future. Researchers worldwide have been working since the end of the last century on the development and application of quantum computation.