Fraunhofer Institute for Integrated Circuits IIS
Fraunhofer Institute for Integrated Circuits IIS
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AI for Quantum Computing

Unlock the potential of quantum computing with AI

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!

Our approach

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.

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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.

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Quantum Algorithms

We focus on the adaptation and development of quantum algorithms for the domains of (quantum) simulation, optimization and machine learning. Our work involves designing quantum algorithms that exploit quantum computing’s unique properties to solve problems that are computationally intensive or even intractable for classical methods. We also explore how these solutions can be seamlessly integrated into existing workflows and pipelines, preparing industries for the transformative potential of quantum-computing enhanced solutions.

Quantum Circuit Cutting

Our research aims to overcome hardware limitations in quantum computing by developing advanced circuit-cutting techniques. These methods enable large quantum computations to be divided into smaller, manageable tasks that can be distributed across multiple quantum processors. By optimizing the process of circuit division and the subsequent reconstruction of results, we address key scalability challenges in quantum computing. Circuit cutting not only supports the execution of complex algorithms on today’s limited quantum computers but also lays the groundwork for distributed quantum computing architectures, making it a cornerstone for scaling quantum technologies.

Quantum Circuit Compilation

We are advancing the field of quantum circuit compilation by optimizing the translation of quantum algorithms into executable instructions for diverse quantum hardware platforms. By further improving the efficiency and accuracy of this process, we ensure that even complex quantum circuits can be executed effectively. This work is vital for bridging the gap between theoretical quantum algorithms and their practical implementation, contributing to the realization of functional quantum systems.

Quantum Error Correction

Error correction is a critical component in making quantum computers reliable and scalable. We develop innovative methods for adapting error correction techniques to both the specific hardware characteristics of quantum processors and the requirements of particular applications. Our focus is on reducing resource demands while maintaining high levels of fault tolerance, making error correction more practical for near-term devices. By tailoring error correction codes to real-world quantum hardware and application needs, we address fundamental barriers to achieving fault-tolerant quantum computing. This work is key to ensuring robust quantum computations and accelerating the deployment of quantum technology in industrial and scientific applications.

Our Service Portfolio

Fraunhofer IIS is your partner to evaluate the potential of quantum computing-based solutions for complex challenges in your business processes.

Research/Development

We offer you partial or complete R&D services.

 

Consulting

We offer you consulting and best practices to promote an effective Quantum DevOps culture.

Feasibility and Technology Studies

We offer you studies to evaluate quantum computing solutions and their economic potential.

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Consulting Workshop

Are you looking to get into quantum computing or are you already working on proof-of-concept solutions in the field of quantum computing?

 

We would be happy to offer your company a customised workshop!

Please contact us!

We realise the efficient processing of your R&D projects as well as the training of junior staff with this new competence profile.

Contact us for a personalised consultation:

Projects

 

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QACI

 

 

As part of the QACI consortium (Quantum Algorithms for Application, Cloud & Industry), we are researching machine learning-based approaches and methods for optimal use of NISQ hardware through optimized compilation.

QACI
 

BayQS

 

 

In the Bavarian Competence Center Quantum Security and Data Science (BayQS), we are researching the use of quantum computers to improve machine learning.

BayQS
 

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QuaST

 

 

In the QuaST research project, we are developing tools to improve the exploitation of quantum computers and paving the way for distributed quantum computing.

QuaST
 

QLindA

 

 

In the project QLindA we are developing quantum reinforcement learning algorithms for industrial applications 

QlindA
 

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Bench-QC

 

 

In the Munich Quantum Valley lighthouse project Bench-QC, we are exploring benchmarking methods to identify practical quantum advantages.

Bench-QC
 

KID-QC²

 

 

In the KID-QC^2 project, a lighthouse project of Munich Quantum Valley, we and the University of Augsburg are working closely together to take the design and optimisation of quantum circuits for quantum chemical calculations to a new level.

KID-QC²

Further information

ML4POS

Indoor positioning/position estimation with deep learning that replaces the signal processing chain.

ML4POS

Autonomous systems

Automation has become a ubiquitous part of our everyday lives and takes the burden off the user – that is, until a problem occurs. These errors can now be rectified by the machines themselves.

Autonomous systems

BayQS - Fraunhofer AISEC

The Bavarian Competence Center Quantum Security and Data Science (BayQS) researches relevant software problems in the context of quantum computing.

BayQS

We are looking for students...

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Online magazine

Series: Quantum Technologies

Article Magazine

Our Publications

Meyer, Nico; Ufrecht, Christian; Yammine, George; Kontes, Georgios; Mutschler, Christopher; Scherer, Daniel D. (2025): 

Benchmarking Quantum Reinforcement Learning

in: 2025 arXiv.org, pp. 1-29.

 

Richter, Martin; Dubey, Abhishek Y.; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D.; Hartmann, Michael J. (2025): 

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

in: 2025 arXiv.org, pp. 1-13.

 

Wiedmann, Marco; Periyasamy, Maniraman; Scherer, Daniel D. (2024): 

Fourier Analysis of Variational Quantum Circuits for Supervised Learning

in: 2024 arXiv.org, pp. 1-12.

 

Meyer, Nico; Berberich, Julian; Mutschler, Christopher; Scherer, Daniel D. (2024): 

Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization

in: 2024 arXiv.org, pp. 1-10.

 

Rietsch, Sebastian; Dubey, Abishek Y.; Ufrecht, Christian; Periyasamy, Maniraman; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D. (2024): 

Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning

in: 2024 arXiv.org, pp. 1-12.

 

Meyer, Nico; Röhn, Martin; Murauer, Jakob; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel 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, Nico; Murauer, Jakob; Popov, Alexander; Ufrecht, Christian; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D. (2024): 

Warm-Start Variational Quantum Policy Iteration

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

 

Meyer, Nico; Ufrecht, Christian; Periyasamy, Maniraman; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D.; Maier, Andreas (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, Christian; Herzog, Laura S.; Scherer, Daniel D.; Periyasamy, Maniraman; Rietsch, Sebastian; Plinge, Axel; Mutschler, Christopher (2024): 

Optimal joint cutting of two-qubit rotation gates

in: 2024 arXiv.org, pp. 1-9.

 

Periyasamy, Maniraman; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D.; Mauerer, Wolfgang (2024): 

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

in: 2024 arXiv.org, pp. 1-12.

 

Herzog, Laura S.; Wagner, Friedrich; Ufrecht, Christian; Palackal, Lilly; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D. (2024): 

Improving Quantum and Classical Decomposition Methods for Vehicle Routing

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

 

Seitz, Philipp; Geiger, Manuel; Ufrecht, Christian; Plinge, Axel; Mutschler, Christopher; Scherer, Daniel D.; Mendl, Christian B. (2024): 

SCIM MILQ: An HPC Quantum Scheduler

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

 

Periyasamy, Maniraman; Hölle, Marc; Wiedmann, Marco; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher (2024): 

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

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

 

Meyer, Nico; Ufrecht, Christian; Periyasamy, Maniraman; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher (2024): 

A Survey on Quantum Reinforcement Learning 

in: 2024 arXiv.org, pp. 1-83.

 

Ufrecht, Christian; Periyasamy, Maniraman; Rietsch, Sebastian; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher (2023): 

Cutting multi-control quantum gates with ZX calculus

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

 

Meyer, Nico; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher; Hartmann, Michael J. (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, Nico; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher; Hartmann, Michael J. (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, Marco; Hölle, Marc; Periyasamy, Maniraman; Meyer, Nico; Ufrecht, Christian; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher (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, Maja; Wolf, Lucas; Periyasamy, Maniraman; Ufrecht, Christian; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher; Mauerer, Wolfgang (2022): 

Uncovering instabilities in variational-quantum deep Q-networks,

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

 

Periyasamy, Maniraman; Meyer, Nico; Ufrecht, Christian; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher (2022): 

Incremental Data-Uploading for Full-Quantum Classification 

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