We are developing a platform and ecosystem for Quantum-inspired 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.
News about PlanQK
There are a number of development environments (or SDKs – Software Development Kits ) for quantum software (see , ): IBM offers QisKit, Rigetti provides Forest, DWave provides Ocean, etc. The development environments of these manufacturers of quantum computers generally only support their own hardware.
The Association of the Software, Information and Communications Industry in Berlin and Brandenburg e.V. (SIBB) is a new associated partner of the PlanQK project. As the association of the digital economy in Berlin and Brandenburg, the SIBB represents more than 250 companies and academic institutions of all sizes. PlanQK thus gains a strong regional partner for interaction with economic and political decision makers.
Two of the manufacturers of large quantum computer systems, IBM and Google, have published ambitious roadmaps in quick succession for the availability of systems with significantly more qubits than today. In mid-September, IBM presented its current development strategy in its Reasearch Blog, which envisages a continuous increase in the number of Qubits in the next quantum chips up to 1121 Qubits in the Condor model by 2023.
For two days the 15 PlanQK consortium partners met in virtual conference rooms in May 2020 to discuss progress on the consortium level and breakout sessions were used to work in smaller groups on detailed topics. With the help of virtual coffee breaks the informal exchange could be facilitated.
Not only is the sheer volume of data stored and processed worldwide constantly increasing. The complexity of the tasks to be solved is increasing to the same extent. Portfolio optimization, risk management and fraud detection in the financial sector, the evaluation of network capacities in the energy industry or the development of new vaccines and drugs in the pharmaceutical sector are just a few examples.
StoneOne and the University of Stuttgart presented the new project PlanQK (Platform and Ecosystem for Quantum-Assisted Artificial Intelligence) to a broad public for the first time at the Digital Summit in Dortmund on October 28 and 29. A physical quantum system from IBM attracted the interest of the visitors at the stand. IBM is an associated partner of PlanQK and contributes appropriate hardware.
Communal Registers AI
Automated linking of citizen information from so-called specialist procedures that are still separate 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.
Duty Schedule Optimization
In duty schedule optimization, personnel resources must be assigned to different tasks. An example of this would be planning 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 the data of older duty rosters and their short-term changes.
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 sums. 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 uniform 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-minimum 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 numerous related graph problems, formulations exist as optimization problems that can be solved or their solutions improved on the basis of 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 time allowed. 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 identify the probability of a contract change from the customer's behaviour. Model-driven AI approaches can be used here, which learn a behavior model from customers and thus act similar to methods of predictive maintenance. Training an AI for concrete countermeasures (better offers, more direct contact, etc.) is promising here and could be supported by Quantum Reinforcement Learning, for example.
“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