Quantum Readiness for Optimization Providers
In the ProvideQ project, world-leading service providers in the field of logistics are cooperating with experts in software and algorithm engineering, optimization theory and quantum information. Together they develop new concepts and methods for bridging the gap between industrial applications and requirements on the one hand and the practical use of innovative quantum computers on the other. The project involves providers of algorithmic services to make them ready for the quantum age, with the goal of benefitting larger sectors of the German economy from the advantages of quantum computing (QC) through their multiplier effect.
Challenge and innovation
The complexity of many industry-relevant optimization problems quickly brings even classical high-performance computers to their limits. Especially for numerous practical problems in logistics, this represents an obstacle to the efficient use of resources. There are already entire libraries of specialised optimisation algorithms that, if used correctly, can achieve massive efficiency gains. However, industrial users face the challenge that while they have detailed knowledge about the optimisation problems in their operations, the highly complex landscape of specialised algorithms does not fall within their core competence. In addition, such users have insufficient knowledge of the potential and applicability of quantum computers. This gap is closed by algorithmic service providers, that will be enabled to offer suitable quantum algorithms for suitable problem classes with the help of the ProvideQ Toolbox.
In order to make algorithmic service providers ready for the quantum age, the ProvideQ project works on two levels. (1) The existing modeling systems of two service providers involved in the project will be extended by a quantum toolbox. This will allow a broad group of users to describe their problems in a domain-specific language, for which solution strategies are then found and implemented on quantum computers or on a combination of quantum and classical computers. (2) In selected areas of integer and convex optimization, as well as for optimization problems under uncertainty, approaches that have so far only been described theoretically will be made practicable and new algorithms will be developed. The intended developments are based on concrete application cases and example data of the project partners and will be made freely accessible.
TU Braunschweig (project lead), Leibniz Universität Hannover, Universität zu Köln, GAMS Software AG, 4flow AG, Johannes Kepler Universität Linz (subcontract)
January 2022 – December 2024
Total costs: € 2.9 million
Funding volume: € 2.3 million