AI for Quantum Computing

Quantum computing has the potential to revolutionise many areas of science and business by overcoming unsolvable problems with classical computers. However, in order to realise this potential, we need to be able to develop quantum algorithms efficiently and implement them on real quantum hardware.

Our work focuses on the further development of state-of-the-art quantum algorithms and the creation of innovative tools for the optimised use of quantum hardware. The aim is to close the gap between theoretical potential and practical application and to enable quantum computing solutions for real-life application scenarios.

Today's quantum hardware is characterised by interference-prone qubits and limited coherence times, posing significant challenges. To fully exploit this new technology, we need to develop advanced algorithms and tools that maximise performance while minimising resources. The quantum hardware of the future will eventually unleash the full potential of quantum computing.

We are using artificial intelligence methods to overcome these challenges. This dual approach - with current and future hardware - ensures that quantum computing can fulfil its promise to transform industries, science and technology: Quantum computing, now!

How are we involved with quantum computing technology at Fraunhofer IIS – Department of Localization and Networking?

Based on our expertise in the fields of quantum computing and machine learning, in particular deep reinforcement learning, we are developing both software tools and algorithms to make quantum computers usable for industrial applications in the near future. Dr Daniel Scherer, Head of Quantum Compilation Group, emphasises that the interdisciplinary collaboration of our team of experts in physics, computer science and mathematics makes essential contributions to the quantum computing software stack. Our goal is to create promising solutions that make quantum computing, especially fault-tolerant and error-corrected hardware, easily accessible while maximising computing power.

Our quantum computing research areas at a glance:

• Quantum Algorithms - We conduct research into quantum algorithms for problems in the fields of simulation, optimisation and machine learning.
• Quantum Circuit Cutting – We research and develop methods for "cutting" quantum circuits. This enables a special form of distributed quantum computing.
• Quantum Circuit Compilation - We are working on the optimised translation of quantum circuits into executable quantum computer instructions.  
• Quantum Error Correction - We are researching methods for generating error correction to reduce the requirements for error-corrected and error-tolerant quantum computers.

The quantum computing stack comprises software and hardware required to execute quantum algorithms on a quantum computer. In the Localisation and Networking department of Fraunhofer IIS, research and development is focused on quantum computing middleware: "Quantum Circuit Cutting", "Compilation" and "Quantum Error Correction" are our main areas of research. We also research quantum algorithms for problems in the fields of simulation, optimisation and machine learning.

van der Horst, L., Periyasamy, M., Dubey, Y.A., Bincoletto, D., Kottmann, J.S., Scherer, D. (2026)

A Transferable Machine Learning Approach to Predict Optimized Orbitals for Electronic Structure Problems

in: arXiv:2605.04174

Meyer, N., Mutschler, C., Seuß, D., Maier, A., Scherer, D. (2026)

Learning Logical Operations for Arbitrary Quantum Error Correction Codes

in:arXiv:2605.28162

Meyer, N., Mutschler, C., Seuß, D., Maier, A., Scherer, D. (2026):

Learning to Concatenate Quantum Codes

in: arXiv:2604.14931

Popov, A., Meyer, N., Scherer, D., Dielt, G. (2026):

Optimized Compilation of Logical Clifford Circuits

in: arXiv:2602.12831

Mattick, A., Periyasamy, M., Ufrecht, C., Dubey, Y. A., Mutschler, C., Plinge. A., Scherer, D. (2025):

Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks

in: arXiv preprint arXiv:2504.03429.

Wiedmann, M., Periyasamy, M., Scherer, D. (2025):

Fourier Analysis of Variational Quantum Circuits for Supervised Learning

in: IEEE QCE 2025 

Periyasamy, M., Ufrecht, C., Scherer, D., Mauerer, W. (2025):

CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting

in: IEEE QML@QCE Workshop 2025

Meyer, N., Mutschler, C., Maier, A., Scherer, D. (2025):

Variational Quantum Error Correction

in: IEEE QCE Poster Track 2025

Geißler, F., Stopfer, E., Ufrecht, C., Meyer, N., Scherer, D., Wagner, F., and others (2025):

BenchQC -- Scalable and modular benchmarking of industrial quantum computing applications

in: QUEST-IS 2025

Meyer, N., Ufrecht, C., Yammine, G., Kontes, G., Mutschler, C., Scherer, D. D. (2025): 

Benchmarking Quantum Reinforcement Learning

in: International Conference on Machine Learning (ICML), Vancouver, BC, Canada

Richter, M., Dubey, A. Y., Plinge, A., Mutschler, C., Scherer, D. D., Hartmann, M. J. (2025): 

Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance

in: 2025 arXiv.org, pp. 1-13.

Wiedmann, M., Periyasamy, M., Scherer, D. (2024): 

Fourier Analysis of Variational Quantum Circuits for Supervised Learning

in: 2024 arXiv.org, pp. 1-12.

Meyer, N., Berberich, J., Mutschler, C., Scherer, D. (2024): 

Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization

in: 2024 arXiv.org, pp. 1-10.

Rietsch, S., Dubey, A. Y.; Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D. (2024): 

Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning

in: IEEE International Conference on Quantum Computing and Engineering (QCE24), pp. 1-12.

Meyer, N., Röhn, M., Murauer, J., Plinge, A., Mutschler, C., Scherer, D. (2024): 

Comprehensive Library of Variational LSE Solvers

in: 2024 2nd International Workshop on Quantum Machine Learning: From Research to Practice (QML@QCE 2024), pp. 1-4.

Meyer, N., Murauer, J., Popov, A., Ufrecht, C., Plinge, A., Mutschler, C., Scherer, D. (2024): 

Warm-Start Variational Quantum Policy Iteration

in: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 1-9.

Meyer, N., Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D., Maier, A. (2024): 

Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks,

in: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 1-7.

Ufrecht, C., Herzog, L.,  Scherer, D., Periyasamy, M., Rietsch, S., Plinge, A., Mutschler, C. (2024): 

Optimal joint cutting of two-qubit rotation gates

in: 2024 arXiv.org, pp. 1-9.

Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D., Mauerer, W. (2024): 

Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule

in: IEEE International Conference on Quantum Computing and Engineering (QCE24), pp. 1-12.

Herzog, L., Wagner, F., Ufrecht, C., Palackal, L., Plinge, A., Mutschler, C., Scherer, D. (2024): 

Improving Quantum and Classical Decomposition Methods for Vehicle Routing

in: 2024 arXiv.org, pp. 1-10.

Seitz, P., Geiger, M., Ufrecht, C., Plinge, A., Mutschler, C., Scherer, D., Mendl, C. (2024): 

SCIM MILQ: An HPC Quantum Scheduler

in: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 292-298.

Periyasamy, M., Hölle, M., Wiedmann, M., Scherer, D., Plinge, A., Mutschler, C. (2024): 

BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading

in: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1-9.

Seitz, P., Geiger, M., Ufrecht, C., Plinge, A., Mutschler, C., Scherer, D., Mendl, C. (2024)
SCIM MILQ: An HPC Quantum Scheduler
Workshop on Integrating High-Performance and Quantum Computing (WIHPQC24) at IEEE International Conference on Quantum Computing and Engineering (QCE24)

Meyer, N., Ufrecht, C., Periyasamy, M., Scherer, D., Plinge, A., Mutschler, C. (2024): 

A Survey on Quantum Reinforcement Learning

in: 2024 arXiv.org, pp. 1-83.

Ufrecht, C., Periyasamy, M., Rietsch, S., Scherer, D., Plinge, A., Mutschler, C. (2023): 

Cutting multi-control quantum gates with ZX calculus

in: 2023 Quantum, Volume 7, pp. 1147-1160.

Meyer, N., Scherer, D., Plinge, A., Mutschler, C., Hartmann, M. (2023): 

Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning

in: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 36-41.

Meyer, N., Scherer, D., Plinge, A., Mutschler, C., Hartmann, M. (2023): 

Quantum Policy Gradient Algorithm with Optimized Action Decoding,

in: 2023 Proceedings of the 40th International Conference on Machine Learning (ICML), pp. 1-22.

Wiedmann, M., Hölle, M., Periyasamy, M., Meyer, N., Ufrecht, C., Scherer, D., Plinge, A., Mutschler, C. (2023): 

An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients

in: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 450-456.

Franz, M., Wolf, L., Periyasamy, M., Ufrecht, C., Scherer, D., Plinge, A., Mutschler, C., Mauerer, W. (2022): 

Uncovering instabilities in variational-quantum deep Q-networks,

in: 2022 Journal of the Franklin Institute, Volume 360, Issue 17, pp. 13822-13844.

Periyasamy, M., Meyer, N., Ufrecht, C., Scherer, D., Plinge, A., Mutschler, C. (2022): 

Incremental Data-Uploading for Full-Quantum Classification

in: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 31-37.