Research

The performance of Machine Learning can be increased even further by using quantum computers. Examples of this include quantum-based price prediction for construction machinery, image recognition in manufacturing and fraud detection in the financial sector – use cases that are the focus of our research work. In detail, we are researching which data is particularly suitable for quantum-based processing and how Quantum Machine Learning models must be designed to be as robust and efficient as possible for training. In the project "AutoQML", we are automating the selection of the associated hyperparameters and the construction of the corresponding (Q)ML pipeline as easily as possible. 

Qubits are extremely fragile, and this limits how long they are useful for quantum computing. Decoherence, the decay in quality of Qubits, is the primary cause for this fragility. Fault-tolerant quantum computing refers to the framework of ideas that allows qubits to be protected from quantum errors. Quantum Error Correction (QEC) serves as a remedy to protect qubit information by encoding the logical qubit (a bit of quantum information) into an ensemble of physical qubits such that a failure of any physical qubit does not corrupt the underlying logical information. Our research goals here are to design appropriate quantum algorithms to implement both QEC and encoded logic operations in a way to avoid quantum errors cascading through quantum circuits for different applications. 

Quantum Computing (QC) has major potential for new concepts regarding service-oriented business models. Therefore, our future vision is to establish innovative QC solutions as "Quantum Services". By researching this "QC as a Service" (QCaaS) approach as part of the project "Quantum Computing Heilbronn-Franken", we will in the future empower companies to integrate QC within their existing IT workflows. To conduct this research, we operate our own classical high-performance computing cluster (HPC cluster), which is among others in the project "AutoQML" used for the simulation of quantum circuits and for the development of (Q)ML models. To demonstrate the potential of "Quantum Service Engineering", the QC tools, frameworks and software demonstrators developed in the research projects will be hosted on our servers as "Quantum Services" and made also available via the "Fraunhofer-GitLab". Furthermore, we offer a practical introduction to the topic inside our modern "Quantum Software Lab" in the context of the Quantum Computing Training Program.

Quantum Machine Learning 

• H. Stühler, D. Klau, M.-A. Zöller, A. Beiderwellen-Bedrikow and C. Tutschku, End-to-End Implementation of Automated Price Forecasting Applications, in SN Computer Science, Vol. 5(402), 2024,
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• H. Stühler, D. Pranjić and Christian Tutschku, Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications, 2024, 
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• D. Klau, H. Krause,  D. A. Kreplin, M. Roth, C. Tutschku and M. Zöller, AutoQML – A Framework for Automated Quantum Machine Learning, 2023,
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• J. Berberich, D. Fink, D. Pranjić, C. Tutschku and C. Holm, Training robust and generalizable quantum models, 2023,
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• D. Klau, M. Zöller and C. Tutschku, Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms, 2023, 
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• H. Stühler, M.-A. Zöller, D. Klau, A. Beiderwellen-Bedrikow and C. Tutschku, Benchmarking Automated Machine Learning Methods for Price Forecasting Applications, 2023,
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Quantum Software Engineering

• C. Tutschku, A. Sturm, F. Knäble, B. C. Mummaneni, D. Pranjic, C. Stephan, D. B. Mayer, G. Koßmann, M. Roth, P.-A. Matt, A. Grigorjan, T. Wellens, K. König, M. Beisel, F. Truger, F. Shagieva, O. Denninger and S. Garhofer, Quantencomputing in der industriellen Applikation. Vom Algorithmen-, Markt- und Hardwareüberblick hin zu anwendungszentriertem Quantensoftware-Engineering, 2023
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• N. Schillo and A. Sturm, Quantum Circuit Learning on NISQ Hardware, 2024, Link to the publication
• A. Sturm, B. C. Mummaneni and L. Rullkötter, Unlocking Quantum Optimization: A Use Case Study on NISQ Systems, 2024,
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• N. Schillo, Quantum Algorithms and Quantum Machine Learning for Differential Equations, 2024,
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• G. Koßmann, L. Binkowski, C. Tutschku and R. Schwonnek, Open-Shop Scheduling With Hard Constraints, 2023,
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• A. Sturm, Theory and Implementation of the Quantum Approximate Optimization Algorithm: A Comprehensible Introduction and Case Study Using Qiskit and IBM Quantum Computers, 2023,
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