PlanQK develops AI on quantum computers
A major project in Germany is using quantum computing techniques to develop quantum AI algorithms.
The €19m PlanQK project (Platform and Ecosystem for Quantum-Assisted Artificial Intelligence) is funded by the German Federal Ministry of the Economy and now has 15 partners and 33 associated partners, including Siemens and Daimler.
The latest member, Deutsche Telekom (DT), is researching and testing potential use cases from the perspective of a network operator. This includes network security as well as data routing algorithms.
Quantum computers promise an exponential increase in processing speed for selected problem classes. For example, in combinatorial optimization problems or the training of AI models. In communication science, Shor’s algorithm is usually considered the “killer application” of quantum computing as the systems can use it to attack today’s security infrastructures.
In the PlanQK project, DT’s Telekom Innovation Labs (T-Labs) provides some specific use cases from the field of telecommunications. These include the optimization of communication networks, Industry 4.0 applications or AI-clustering problems for particular applications.
These applications have a high level of complexity and, if the problem exceeds a critical size, can only be calculated classically with great difficulty. Here, quantum algorithms promise the solution. With growing size, quality and processing speed, quantum computers could find their way into Telekom’s operational business.
The project is developing a vendor-independent platform and associated ecosystem for quantum AI. Users could then, for example, compile solutions for their company or commission them via the cloud or a quantum app store.
Next: hardware-agnostic quantum AI SDK
PlanQK follows a completely different path to the software development kits provided by quantum computer developers, as it is not a development environment for quantum software and is intended to be absolutely SDK neutral. The software provided in PlanQK can be created with any such SDK by using the concept of patterns, proven solutions to recurring problems in quantum computing. By definition, the solutions provided in such patterns are implementation- and vendor-neutral. A pattern then refers to possibly different quantum AI implementations created by “any” quantum SDK.
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