Join QuCUN and take your use case to the next level with quantum computing
Start your quantum journey with us
Whether you're new to quantum computing or already have a use case in mind, we're here to guide you. No experience? We'll help you discover quantum possibilities. Have ideas? Let's explore them together.
Build together, learn together
Our quantum experts work side-by-side with your team to develop a proof of concept. We share our knowledge while you gain hands-on experience, ensuring you understand every step of the quantum development process.
Evaluation of the results
Our quantum computing experts analyze and comment on the results of the developed proof of concept.
Publication in our Use Case Library
Once the project is completed, we publish the PoC in our Use Case Library. Your contribution enriches the quantum computing community and provides learning opportunities and inspiration for others.
Success Stories
See how organizations have successfully partnered with us to explore quantum computing solutions for their specific challenges.
Schaeffler Success Story
The challenge piloted with QuCUN involves robots selecting individual objects from disorganized bins containing items of varying sizes and orientations.
Current methods for solving Bin Picking rely heavily on machine learning, particularly in image recognition, to detect and identify objects. New approaches aim to achieve the same performance with cheaper, consumer-level cameras for pose detection. These algorithms require substantial training data, which is increasingly generated synthetically to eliminate the need for manual data labeling.
"Partnering with QuCUN, exploring the capabilities of Quantum Computing, confirmed to us that Quantum algorithms have the potential to improve the process of detecting object locations and orientations, enhancing both precision and speed.. The lead time of the project and the speed of project delivery, coordinated by the QuCUN Contact Office was of tremendous help and value."
— Michael Schlotter, Director of SHARE at FAU – AI Research
BASF Story
As a member of the QuCUN consortium, BASF has proven to be an ideal first test customer, actively contributing to the development of the Molecular Dissociation Use Case available on the platform. The dedication of the contact office in working on and implementing this use case has been remarkable. BASF’s motivation is to explore relevant use cases in the chemical industry early on. By collaborating with QuCUN and sharing applications, we aim to expand the German quantum computing ecosystem.
Additionally, we utilize the use cases on the QuCUN platform in our internal trainings to demonstrate the practical applications of quantum computing.
"Working with the QuCUN consortium is a great experience. By developing and implementing the Molecular Dissociation Use Case, we’ve shown how quantum computing could be used in the chemical industry in future. BASF is excited to explore and advance this cutting-edge technology to unlock new possibilities and drive innovation."
— Nicole Klein-Stocké, Project Lead QuCUN at BASF
Byte Robotics Success Story
The challenge piloted with QuCUN explores autonomous robot navigation in complex environments, where robots must efficiently plan collision-free paths through dynamic surroundings. Efficient motion planning is a fundamental capability for autonomous robots in logistics, manufacturing, and service robotics, where planning speed and computational efficiency directly impact performance.
Current methods of planning robot motion primarily rely on classical machine learning models to plan collision-free paths. In this QuCUN use case, however, a hybrid quantum-classical approach is being evaluated: a classical neural network processes the environment, while a seven-qubit quantum circuit predicts the robot's next movement. Although the classical planner currently achieves higher success rates, the quantum-enhanced model already makes meaningful navigation decisions using approximately 93% fewer trainable parameters and only around 2% of the training data. This highlights the potential of quantum transfer learning to reduce model complexity while maintaining meaningful navigation performance.
"Working with QuCUN has given us the opportunity to explore how quantum computing could complement established approaches to autonomous robot navigation. The results are promising: achieving meaningful navigation performance with significantly fewer trainable parameters and training data highlights the potential of quantum-enhanced methods for more efficient robotic systems. The collaboration has provided us with valuable insights into this emerging technology and its potential to create real value for robotics in the future."
— Julian-Benedikt Scholle, CTO, byte robotics GmbH
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Ready to transform your business with our innovative solutions? We're here to help you achieve your goals and drive success through collaboration.
