We put out a tender to find support for our QML project QuTeNet. BearingPoint – with Multiverse Computing and Fraunhofer CML as subcontractors – was awarded the contract in the bidding competition. Together with the DLR research team lead by the DLR Institute for AI Safety and Security, the companies are now developing highly efficient tensor network solutions for maritime logistics that can also be applied to many other optimisation problems.
On the path from classical to hybrid to pure quantum computing, tensor networks offer many exciting possibilities. The task of our QuTeNet project lead by the DLR Institute for AI Safety and Security is to research and further develop these – also in comparison to other machine learning approaches.
With two industry contracts (one of which went to Tensor AI), we not only want to make theoretical progress, but also further develop the practical benefits. In the QuTeNet | Tensor Networks project, BearingPoint, Fraunhofer CML and Multiverse Computing are therefore not only realising highly efficient tensor network solutions for maritime logistics, but also creating an efficiently implemented software library. This can be applied to many other optimisation problems in industry and research, encodes general combinatorial quantum optimisation problems and solves them – including the necessary pre- and post-processing – using tensor networks, with or without a quantum computer.
BearingPoint is coordinating the project on the industry side, contributing tensor network expertise and mapping a broad range of applications; Fraunhofer CML is providing real data on ship routes, ports and loads as well as the use case; Multiverse Computing is implementing the optimisation package with its expertise in tensor networks and software development.
QuTeNet is a project lead by the DLR Institute of AI Safety and Security in cooperation with the DLR Institute of Quantum Technologies and the DLR Institute of Software Technologies.
Sensor networks build bridges
Tensor networks enable the classical simulation of quantum states. They thus build a direct bridge between classical and quantum computing approaches. They enable powerful quantum-inspired optimisation solutions on classical hardware and particularly efficient calculations on quantum computers. In this way, optimisation solutions can be developed for classical hardware that are already quantum-ready and can be seamlessly solved even more efficiently on available quantum hardware in the future. With the QuTeNet | Tensor Networks project, we are using a real application case to create a model for how tensor networks are already enabling the transition from classical to quantum computing:
BearingPoint
BearingPoint is an independent management and technology consultancy with European roots and global reach. The company operates in three business units: Consulting, Products and Capital.
