Why Are There Projects to Replace Classical Computing with Quantum Computing in the Transport Sector?

Automotive companies are breaking into the quantum space

Recently, there have been reports about various automotive companies starting collaborations with quantum computing companies.

Earlier this month there have been reports of quantum software company Multiverse Computing joining a Renault-led electric vehicle alliance in Spain. Further, Airbus has recently announced a collaboration with IonQ.

So why are aviation and car manufacturers looking to replace classical computing with quantum computing solutions?

Multiverse Computing is working with the alliance led by Renault on a project entitled “Industrial ecosystem of innovation for electric and connected vehicles in Spain”.

An optimization problem is the problem of finding the best solution for all feasible solutions. A classic example of an optimisation problem is the travelling salesman problem:

"Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?"

Running through every possibly solution soon becomes intractable when there are 20 or more cities. Approximate optimisation techniques exist such as simulated annealing. These give a good solution (for example within 2-3% of the optimum solution) in a reasonable amount of time, but will not reliably give the optimum solution

Thus, optimisation problems require considerable computing resources and sometimes impractical run times if they are solved by classical computing techniques.

However, the quantum property of superposition allows the representation of all possible solutions whereas the quantum property of interference enables the identification of low cost, high-value solutions. Thus, quantum computation algorithms and quantum computing inspired algorithms have the potential to solve optimisation problems much more quickly and with higher accuracy than conventional algorithms for solving optimisation problems.

There is a challenge to obtaining patent protection for algorithms relating to optimisation problems. However, it is not impossible to obtain protection for such algorithms. At the European Patent Office (EPO), it is necessary to show that the invention addresses a technical problem. Therefore, it is possible to protect algorithms for optimisation if they are tied to a technical purpose, e.g. more efficient routing, battery cycle management etc., but this needs to be emphasised in the patent application. It is also possible to obtain patent protection for optimisation problems if there is something new and clever about the way in which the software is interacting with the underlying hardware. This is easy to show if the hardware configuration is new, but it is more challenging to show for software running on conventional platforms.

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