Quantum Readiness for Optimization Providers

ProvideQ, leading global service providers from the field of logistics cooperate 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. In doing so, the project involves providers of algorithmic services to make them ready for the quantum era. The goal is to enable larger sectors of the German economy to benefit from advantages of quantum computing (QC) through their multiplier effect. Challenge and innovation: The complexity of many optimization problems relevant to industry quickly pushes even classical high-performance computers to their limits. In particular, for many practical problems in logistics, this represents an obstacle to efficient resource utilization. It is true that entire libraries of specialized optimization algorithms already exist which, if used correctly, can achieve massive efficiency gains. However, industrial users face the challenge that while they understand in detail the optimization problems in their operations, the highly complex landscape of specialized algorithms is not within their core competency. In addition, such users have insufficient knowledge of the potential and applicability of quantum computers. This gap is served by algorithmic service providers, who are to be enabled with the help of the ProvideQ Toolbox to offer suitable quantum algorithms for suitable problem classes. Approach: To make algorithmic service providers ready for the quantum era, the ProvideQ project addresses 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 will then be found to be implemented on quantum computers or combined on 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 are to be made practicable and new algorithms are to be developed. The intended developments are based on concrete application cases and example data of the project partners and all results are to be made freely accessible.