GAMS Secures BMBF Funding for “QuSol” Project together with partners at KIT, FUB, RUB and Infineon
We are excited to announce that GAMS, in collaboration with Karlsruhe Institute of Technology - KASTEL (KIT-KASTEL), Infineon Technologies AG, Ruhr-Universität Bochum (RUB), and the Freie Universität Berlin (FUB), has been awarded funding from the German Federal Ministry of Education and Research (BMBF) under the “Anwendungsorientierte Quanteninformatik” initiative. The project, known as “QuSol” (Quantum Optimization Solver Kit), focuses on exploring how quantum computing could enhance optimization problems that are central to modern industries, such as production planning and logistics. While the field of quantum computing is still in its early stages, the goal is to advance both theoretical and practical understanding of how quantum algorithms can be applied to solve complex optimization challenges.
Academic Partners
- Prof. Dr. Ina Schaefer, Karlsruhe Institute of Technology (KIT-KASTEL)
- Prof. Dr. Stefan Nickel , Karlsruhe Institute of Technology (KIT-IOR)
- Prof. Dr. Michael Walter , Ruhr-Universität Bochum
- Prof. Dr. Jens Eisert , Freie Universität Berlin
Industry Partners
Associate Partner
- Prof. Dr. Tobias Osborne , Leibniz Universität Hannover
Addressing Classical Limitations with Quantum Solutions
Quantum computers promise to solve problems that classical computers cannot handle efficiently. This potential has spurred significant interest in physics and quantum informatics, with recent advancements enabling the development of mid-scale “Noisy Intermediate-Scale Quantum” (NISQ) computers. The critical question now is identifying key applications where quantum computing can deliver significant advantages—particularly in economically relevant fields like optimization.
Optimization is at the forefront of potential quantum computing applications, as nearly every problem in modern supply chain planning can be framed as an optimization challenge. However, despite the theoretical promise, few concrete findings currently substantiate the expectation that quantum computers will outperform classical methods in practical settings. This gap applies to both variational quantum algorithms, which can already be implemented on near-term NISQ (Noisy Intermediate-Scale Quantum) hardware, and scalable quantum algorithms, designed for future error-tolerant quantum computers.
The QuSol Project: Pioneering Quantum Optimization
The QuSol project brings together leading experts to achieve a disruptive breakthrough in what quantum computers can accomplish in the field of optimization. By focusing on concrete, economically critical use cases in production planning, the project seeks to substantially expand the algorithmic toolbox for optimization using quantum computers, ultimately making quantum computing a practical tool for solving real-world problems.
QuSol’s ambition goes beyond addressing isolated problems. It aims to develop generic hybrid solution methods that can tackle complex optimization problems with uncertainties. By incorporating quantum algorithms, these methods will provide more efficient solutions than classical approaches alone. The project will create a reusable, adaptable open-source software package that allows the broader community to apply and further develop these quantum-powered optimization tools across various disciplines.
Impact on Optimization and Beyond
The project will build on previous initiatives but with a broader and more ambitious scope. Prior projects have focused on specific, limited use cases, while QuSol will offer a comprehensive, quantum-enhanced optimization toolkit applicable to a wide array of complex problems. This toolkit will accelerate optimization processes not only in production planning but across industries facing uncertainty and high complexity in their operations.
The QuSol project is driven by highly relevant real-world applications in modern production planning, where the current global situation and associated uncertainties make optimization more challenging and essential than ever. Well-defined, representative use cases from experts in classical and quantum optimization will be analyzed and broken down into subproblems that can be solved more efficiently with variational and/or scalable quantum algorithms. The project will not only adapt and extend existing quantum algorithms but also develop new ones, assessing their applicability and advantages for different problem instances.
©Infineon Technologies AG, 2024
Fig 1: Relevant example for a complex optimization problem in production planning. In the capacity-demand match (also known as master planning), a demand predicted by the demand planning side is set against constraints (called bottlenecks) set by the capacity planning side. The aim is to determine production targets that satisfy the capacity constraints and can therefore be achieved later in a detailed production plan. These targets should be matched as closely as possible to demand and are used in the form of available-to-promise (ATP) quantities to confirm customer orders at a later date.
Building the Future of Quantum-Enhanced Optimization
QuSol’s results will be integrated into a state-of-the-art, open-source software package, enabling a wide user community to take advantage of the advancements. This software will serve as a key building block in the evolving quantum ecosystem, allowing for further innovation and practical application of quantum optimization across disciplines. The project’s outcomes will bring significant practical benefits by addressing a broad spectrum of optimization challenges.
We are thrilled to contribute to this pioneering initiative, which promises to reshape the future of optimization technology and bring quantum computing closer to real-world applications.
