Contractor wanted for QCOptSens | Cross compiler project

We are looking for companies to support our QCOptSense project in the development of new approaches to improve optical instruments for aerospace with quantum computers. Participation in the tender process is possible via TED 226693-2024. The submission deadline is 24 May 2024 at 2 pm. We have published the tender documents and specifications on subreport ELViS.

The DLR Institute of Optical Sensor Systems (DLR OS) has been successfully developing camera systems and spectrometers for aerospace, security and transport applications for many years. These highly complex instruments contain a large number of optical, mechanical, electronic and software components that have to be harmonised in detail and controlled during operation in order to guarantee high data quality. These highly complex instruments contain a large number of optical, mechanical, electronic and software components that have to be harmonised in detail and controlled during operation in order to guarantee high data quality.

These strict requirements against the backdrop 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, for example in hazardous 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 considerable amounts of training data. Theoretical guarantees and robustness are still difficult and generalisation properties are based on empirical results. The search for quasi-optimal AI models is becoming increasingly complex and in some cases can only be carried out with considerable effort. For many use cases, this will no longer be sustainable in the foreseeable future, and disruptive technologies such as quantum machine learning will become increasingly relevant here.

Collaboration in the QCOptSens project

With this call for proposals for Automated Code Transformation for Quantum Machine Learning, we are looking for industrial partners to investigate and assess the potential of new techniques for semi-automated and AI-supported code transformation and hybridisation. 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 computation in the near and longer term. Methods will be proposed, implemented and evaluated using examples from the field of thematic content reconstruction with hazard context.

The DLR Institute of Optical Sensor Systems (DLR OS) is given a classification task in the field of multi- and hyperspectral satellite images in the area of hazard detection with regard to data set, functionality and boundary conditions as a basic function. The industrial partner adds its own classification task from the field of satellite image analysis. After each step of the industry partner, an executable platform must be available for both tasks and boundary conditions must be defined and discussed.

The specific tasks are

  • Analysing the fundamental solvability of the problem set by DLR OS and developing your own proposals for implementing and, if necessary, reducing the problem,
  • Erzeugung eines DSL-Codes (Domain Specific Language) für die von DLR OS vorgegebene Klassifikationsaufgabe,
  • Generation of a DSL code (Domain Specific Language) for the classification task specified by DLR OS,
  • Compilation, execution and delivery (legacy, classic mapping, QC mapping).
  • The use of the applicant’s cross-compiler technology should be offered via a corresponding platform of the applicant.

All information can be found in the tender documents including the service description on subreport ELViS.