Contractor wanted for QuTeNet | Quantum AI and quantum simulation project

We are looking for contractors for our QuTeNet project to support us in the implementation of tensor network-based methods for quantum computing applications. Participation in the tender process is possible via TED 202129-2024. The submission deadline is 6 May 2024 at 2 pm.

The potential of quantum computers for tensor networks

Our project QuTeNet evaluates the advantages and disadvantages of quantum tensor networks compared to classical networks for applications in quantum simulation and quantum AI and investigates the applicability of quantum tensor networks on real quantum hardware. It develops concepts and methods to implement and analyse quantum tensor networks, evaluates the development possibilities of tensor network methods on classical computers and deals with concrete use cases in the simulation of quantum systems.

Collaboration in the QuTeNet | Quantum AI and Quantum Simulation project

To participate in the QuTenet project, we are looking for an industrial partner to help us implement tensor network-based methods for quantum computing applications, in particular for the implementation of quantum AI and quantum simulations, as well as for the simulation of quantum computers on classical hardware.

In concrete terms, this means, among other things

  • Collaboration in the implementation of resource-efficient tensor operations for classic HPC architectures,
  • Setting up and providing representative benchmark cases
  • Conceptual work on the mapping of tensor network-based AI models on quantum hardware and on the correctness of the learning procedures as well as the operationalisation of the developed methods
  • Implementation of the developed TN-based advanced quantum AI methods and quantum algorithms on simulators and quantum hardware
  • The demonstration of use cases for tensor networks for the simulation of quantum systems using HPC/quantum hardware,
  • The participation in the exploration of perspective applications of tensor networks, and
  • The development of DLR employees’ expertise through literature research, workshops and training courses.

Further information on the work packages can be found in the specifications (Part 2 of the tender documents).