The automotive industry is undergoing a period of profound transformation, compelling its players to reinvent themselves and redefine the purpose of their business. This transformation is being driven by two key factors. Firstly, there are urgent climate goals that call for alternative propulsion technologies. This includes the need for solutions that move in the direction of a de-carbonised and circular economy. Secondly are the changing expectations of consumers who are shifting their preferences towards sustainable and technological choices, one of which is autonomous driving.
The challenge for manufacturers is to implement major alterations on multiple company levels to find efficient solutions for vehicle development and production that are as cost-effective as possible. To achieve this, car makers are increasingly looking to incorporate quantum computing into their solutions which is capable of generating benefits perfectly in line with the needs of the industry. The following are just a few examples of how quantum computing can help the automotive industry transform itself for the challenges of a more sustainable, more autonomous future.
Optimising sensor set-up
Modern vehicles are equipped with numerous sensors to interact with their surroundings but positioning them is an extremely complex task. An error could lead to a needless excess of sensors, very high costs, or reduced vehicle safety. Quantum computing can optimise the process as it can analyse a multitude of combinations to ensure the sensing devices are positioned most efficiently.
In addition, quantum calculations make it possible to better understand which types of sensors are needed and how they should be oriented. The areas covered by the sensors are also crucial. The optimal configuration must ensure that all areas are adequately covered and that the most critical ones are observed simultaneously by several sensors of different types. This too is a task quickly solved by quantum computing.
Boosting testing efficiency
Testing new vehicles is essential to ensure their safety and quality, however, it is a very costly phase for car manufacturers. Here, they are faced with a two-level combinatorial problem. Optimal vehicle configurations must be determined to test all its components, but the tests must take place in such a sequence as to ensure the maximum possible number of tests for each product. Quantum computing is particularly useful because it promotes flexibility, efficiency, and scalability by identifying the lowest number of test vehicles and allowing the highest number of tests at the same time.
Improving quality control
Currently, manufacturers use machine learning to create images during the production process that enable them to quickly visualise and identify defective parts, resulting in significant time and costs savings. However, quantum circuits now allow new representations of the data and can be trained much faster. Through a hybrid architecture, large amounts of data can be processed efficiently, using the potential of qubits to train new data encoding. Hybridisation is a very flexible approach and can easily be applied to other machine learning use cases, where data also comes from other sources.
Integrating quantum computing into business processes means taking the quantum leap—a leap which automotive companies must be willing to explore. They must revolutionise themselves to remain competitive on the market, especially against digital native players. Quantum has already proven to be the perfect ally for anyone who wants to focus on the efficiency of their company. The next and critical step remains to move on to its integration.
The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.
Johannes Oberreuter, Manager at Reply, Quantum Computing Practice
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