The potential of quantum information technologies is one of the fundamental and groundbreaking competencies of the future. There is an opportunity to revolutionize key industries with the resulting performance.
Among all technologies, quantum computers represent a strategic, future-relevant and currently almost exponentially growing area of research and development, for which the decisive course is now being set on the national and international stage. In order to advance their development, the DLR Quantum Computing Initiative involves partners from industry and business as well as research. In this way, DLR can procure and operate quantum computers and make them usable for relevant applications and further develop its own expertise.
The project "Quantum Computation for Optical Sensor Design" (QCOptSens) is one of the projects from the Quantum Computing Initiative and is led by the DLR Institute of Optical Sensor Systems (DLR-OS). DLR-OS has been successfully developing camera systems and spectrometers for aerospace, security and traffic for years. These highly complex instruments contain a large number of optical, mechanical, electronic and software components that must be coordinated in detail and controlled during operation to ensure high data quality. These strict requirements against the background of ever-increasing dimensionality in sensor data also require new methods in data processing and content reconstruction in the long term. In addition to high data quality, crucial information, e.g. in dangerous situations, should also be reconstructed and communicated faster than before. AI-based methods already have enormous application potential here.
However, these data-driven techniques also require a complex and often unstable learning process with often significant amounts of training data. Theoretical guarantees and robustness are still difficult, and generalization properties are based on empirical results. The search for quasi-optimal AI models is becoming increasingly complex and can sometimes only be carried out with considerable effort. For many use cases, this will no longer be sustainable or expedient in the foreseeable future, and disruptive technologies such as quantum machine learning are becoming increasingly relevant here.
With this call for proposals ("Automated Code Transformation for Quantum Machine Learning"), DLR is looking for industrial partners to investigate new techniques for semi-automated and AI-supported code transformation and hybridisation and to evaluate their potential. Research and development in this area is essential to keep pace with the rapidly increasing dimensionality and complexity of data and algorithms in the field of quantum computing-based calculations in the near future and also in the longer term. On the basis of examples from the field of thematic content reconstruction with a hazard context, methods are to be proposed, implemented and evaluated.
The industrial participation in the QCOptSens Cross Compiler project is made up of two components. The first part is direct participation in the project, the second part is integration via technology transfer. Further information can be found in the tender specifications attached to the tender documents.

LOT-0001
QCOptSens Cross Compiler.
The potential of quantum information technologies is one of the fundamental and groundbreaking competencies of the future. There is an opportunity to revolutionize key industries with the resulting performance.
Among all technologies, quantum computers represent a strategic, future-relevant and currently almost exponentially growing area of research and development, for which the decisive course is now being set on the national and international stage. In order to advance their development, the DLR Quantum Computing Initiative involves partners from industry and business as well as research. In this way, DLR can procure and operate quantum computers and make them usable for relevant applications and further develop its own expertise.
The project "Quantum Computation for Optical Sensor Design" (QCOptSens) is one of the projects from the Quantum Computing Initiative and is led by the DLR Institute of Optical Sensor Systems (DLR-OS). DLR-OS has been successfully developing camera systems and spectrometers for aerospace, security and traffic for years. These highly complex instruments contain a large number of optical, mechanical, electronic and software components that must be coordinated in detail and controlled during operation to ensure high data quality. These strict requirements against the background of ever-increasing dimensionality in sensor data also require new methods in data processing and content reconstruction in the long term. In addition to high data quality, crucial information, e.g. in dangerous situations, should also be reconstructed and communicated faster than before. AI-based methods already have enormous application potential here.
However, these data-driven techniques also require a complex and often unstable learning process with often significant amounts of training data. Theoretical guarantees and robustness are still difficult, and generalization properties are based on empirical results. The search for quasi-optimal AI models is becoming increasingly complex and can sometimes only be carried out with considerable effort. For many use cases, this will no longer be sustainable or expedient in the foreseeable future, and disruptive technologies such as quantum machine learning are becoming increasingly relevant here.
With this call for proposals ("Automated Code Transformation for Quantum Machine Learning"), DLR is looking for industrial partners to investigate new techniques for semi-automated and AI-supported code transformation and hybridisation and to evaluate their potential. Research and development in this area is essential to keep pace with the rapidly increasing dimensionality and complexity of data and algorithms in the field of quantum computing-based calculations in the near future and also in the longer term. On the basis of examples from the field of thematic content reconstruction with a hazard context, methods are to be proposed, implemented and evaluated.
The industrial participation in the QCOptSens Cross Compiler project is made up of two components. The first part is direct participation in the project, the second part is integration via technology transfer. Further information can be found in the tender specifications attached to the tender documents.