Quantum-inspired algorithms: probably the best differential solvers in the world

Project funded by Carlsberg Foundation (Semper Ardens: Accelerate), grant CF22-1030. Participating organisations named by the source: IT University of Copenhagen. Runs n/a to 2027-01-30. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2025-08-01); national funding data, not CORDIS. Abstract given by the source: What? A lot of research has been devoted to finding quantum algorithms to be run on quantum computers in the future. What has only be realised very recently is that some of the building blocks of these algorithms can also lead to faster classical algorithms to be run on available classical hardware. One area where these quantum-inspired have very recently provided dramatic speedup is in solving partial differential equations. This project aims to develop the full potential of quantum inspired algorithms, and ambitions to dramatically speedup the solution and simulation of partial differential equations in a wide variety of settings. Why? Solving partial differential equations to high precision is essential for many applications in science, engineering and finance, including: chip design, radar cross section, computational fluid dynamics, seismic imaging, quantum chemistry, derivatives pricing in finance, and many more. Globally, a large fraction of high performance computing resources are allocated to solving PDEs. Hence, algorithmic progress will have substantial industrial and scientific impact, while mitigating the CO2 footprint of these massive simulations. How? The essential toolset, upon which the project is built, is tensor networks. Tensor networks were originally developed for simulating strongly correlated quantum materials. They are especially well suited for describing high dimensional objects that contain strong, but ordered, correlations across the dimensions. Solutions to partial differential equations exhibit exactly these sorts of correlations due to their differential origin. Hence, we will leverage a large body of work into a new field, and expect constructive cross-fertilisation.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Carlsberg Foundation. Retrieved 2026-11-02. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project, funded by the Carlsberg Foundation, focuses on developing quantum-inspired algorithms to enhance the speed of solving partial differential equations. It involves the IT University of Copenhagen and is set to run until January 30, 2027. Suggested technology pillar: enabling. Generated by AI from the public source text; it may contain errors and the original source is authoritative.