Our QCI QCoKaIn application project has awarded a research and development contract to Airbus Defence & Space: Together with the research team of the DLR Institute of Data Science, Airbus will work on hybrid algorithms for automatic anomaly detection in telemetry data. The company will contribute the use case and telemetry data on which the jointly developed hybrid algorithms will be tested. The subcontractor is the AI startup Just Add AI from Bremen which supports the maintenance and support of the platform.
Telemetry data is often the only way to identify and analyse the status, behaviour and changes in complex systems during operation and retrospectively. Good telemetry data and powerful analysis methods make it possible to reliably detect both acute misbehaviour and creeping trends and to take the right measures to prevent and resolve faults. However, in view of the large number of data points and the immense growth in data volumes, this is becoming increasingly difficult to achieve in real time.
One possible solution is to use hybrid algorithms to efficiently search for patterns in large data streams using classical and quantum machine learning. This is where the QCoKaIn project comes in, which is building an infrastructure for hybrid quantum high-performance computing on the software side and developing, utilising and evaluating hybrid algorithms for anomaly detection in combination with causal inference.
As a contractor, Airbus is also contributing an algorithm already used by the company, including the associated software pipeline. QCI QCoKaIn is based on this algorithm and is also supported by Airbus in the evaluation of the algorithm and the development of the commercialisation perspective.
Benefits for the ecosystem
For Airbus Defence & Space, which represents the strategy of the entire Airbus Group, the collaboration in QCI QCoKaIn reflects its strategic approach to promoting quantum technologies. Airbus provides its MLOps platform, data and classical machine learning algorithms, while QCI CoKaIn promotes research into hybrid quantum machine learning algorithms. Through this joint approach, the strengths of traditional and quantum-based algorithms can be tested and further developed using a relevant application scenario in anomaly detection and data analysis for space missions.
Airbus is thus pursuing its goal of remaining at the forefront of technological innovation, improving its operational capabilities and expanding its portfolio of solutions. QCI QCoKaIn can transfer scientific know-how into an industrially relevant use case. And together, QCI QCoKaIn and Airbus are advancing the industrial application of quantum machine learning and exploring the usefulness of new methods for real-time anomaly detection for other companies and use cases.
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