We are developing a platform and ecosystem for Quantum-assisted Artificial Intelligence, PlanQK for short. Users will be able to access a quantum AppStore, developers will be able to use quantum platforms in a simple way and specialists will be able to provide concepts that make quantum computing easily accessible.
Development of a Platform and Ecosystem for Quantum-assisted Artificial Intelligence.
PlanQK is supported by well-known companies, scientific institutions and association.
Current news, events and press articles about the PlanQK project can be found here.
Events & webinars around PlanQK and Quantum-assisted Artificial Intelligence.
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Scheduling and duty schedule optimization
In duty schedule optimization, personnel resources must be allocated to different tasks. An example would be the planning of the technical field service or the technical maintenance service. This use case should also take into account that tasks often arise at short notice. For example, a Quantum Boltzmann Machine can be used to train a neural network with data from older duty rosters and their short-term changes.
Municipal registers AI
Automated linking of citizen information from so-called specialized processes that have been separated until today. The event of a change of address at the residents’ registration office should automatically lead to the offer of a new residents’ parking permit and the re-registration of waste disposal, without the systems communicating with each other via standardized interfaces. Thus a kind of recommendation system is created, which in turn can be solved as an optimization problem on quantum computers.
Water anomaly detection in public buildings
Detection of pipe bursts and other faulty operating conditions via noises on the main water supply line. The early detection of pipe bursts, for example in sports halls and schools, is of great importance for administrations and insurance companies because of the potentially high damage amounts. A Quantum Support Vector Machine, for example, can be used here as an application in the area of predictive maintenance.
In order to ensure a smooth operation of the core or access network, the load on all nodes and edges must be minimized. At the planning level, it is important to find a cost-minimizing expansion of the capacity matrix (i.e. minimum network expansion costs) for a given development of the network throughput in order to operate the future volumes in the network with minimum “maximum utilization”. For many related graph problems, formulations exist as optimization problems that can be solved or their solutions improved based on QAOA or Quantum Annealing.
Industrial Production Lines
In a (sheet metal) production facility, it must be decided upon receipt of an order how parts are to be distributed on raw material sheets and when which of these raw material sheets are to be processed on which machine. Of course, the restrictions of the problems must be observed, e.g. a machine can only process one sheet at a time, the number of machines is given and an order must be completed within the scheduled time. This use case can be treated as a decision making problem using Quantum-enhanced Reinforcement Learning or as a linear equation system using HHL.
Customer Behavior Prediction
Customer Churn Prediction” is the ability to recognize the probability of a contract change from the behavior of the customer. Here, model-driven AI approaches can be used, which learn a behavior model from customers and thus act similar to methods of predictive maintenance. Training an AI on concrete countermeasures (better offers, more direct contact, etc.) is promising here and could be supported by Quantum Reinforcement Learning, for example.
News about PlanQK
In August 2021, the first PlanQK Hackathon took place. On the premises of the consortium partner Frankfurt Consulting Engineers (FCE) and in compliance with pandemic regulations, team members of different consortium partners were able to solve challenging quantum computing tasks in mixed teams.
For the second time, members of PlanQK consortium partners were among the winners of the Quantum Future Award: Johannes Jakob Meyer was awarded first place for his master’s thesis. The thesis, entitled „Applications of Parametrized Quantum Circuits on Near-Term Quantum Devices,“ was written under the supervision of Prof. Dr. Jens Eisert of PlanQK consortium partner Dahlem Center for Complex Quantum Systems at Freie Universität Berlin.
Following T-Labs, T-Systems is now also an associated partner of PlanQK. Based on the findings of the research project, the aim is to explore ways of combining the cloud infrastructure with the requirements of the PlanQK platform.
The Bundesverband deutscher Banken e.V. is a new associated partner of PlanQK. The explicit goal is to improve processes in the financial industry by using quantum computing and artificial intelligence.
“A platform for the exchange on the subject of QKI “[…] would be a great thing! It is my wish to get to know more concrete use cases of QC and AI. What kind of effort is being made? Which skills are required? What do I have to do to create the conditions for QC and AI? What are these conditions? How do I finance them?”
Michael Zaddach, CIO, Airport Munich
“Quantum computing and artificial intelligence are two highly relevant and highly complex technologies which, especially when combined, could hold unknown potential for Allianz. I would welcome an exchange on these subjects immensely.”
Ralf Schneider, CIO, Allianz SE
“AI, QC and all that goes with it, cannot be done by anyone alone. This requires partners and networks that are working together. What matters most to us is the respective fields of competence that such a platform covers. We are rather less interested in “classical” consultants, but in consultants who act as implementation consultants, for example in the concrete implementation of solutions or in training.”
Dieter Rehfeld, Chairman of the Board, regio iT GmbH