The QSTAR Center for Quantum Computing and Applications aims to strengthen Norway’s position in the field of quantum computing by advancing fundamental research in key areas where the partners have distinctive expertise, namely fault tolerance, compilation, and quantum algorithms, while building a sustainable national academic community, fostering strong Nordic and European collaborations, and training the next generation of researchers and experts in quantum technology. Click here for more information regarding all quantum centres funded in 2025.

The Gemini Center for Quantum Technology was established in 2020 with a focus on Quantum Computation. In 2024, its scope expanded to include Quantum Sensors and Materials, as well as Quantum Communication and Security. The Center works to coordinate quantum research across Norway and to strengthen Norway’s position as a leading contributor to future quantum technologies. See official website.

iDesignRES is one of the largest EU projects on energy system modelling and optimization. It is dedicated to accelerating the understanding of the insights of energy system analyses. Its objectives focus on developing optimized open-source tools for comprehensive energy system modelling (representing long term planning and short-term operations), and on creating dynamic multi-physics models. See official website.

Abstract

Quantum computing is rapidly advancing, harnessing the power of qubits’ superposition and entanglement for computational advantages over classical systems. However, scalability poses a primary challenge for these machines. By implementing a hybrid workflow between classical and quantum computing instances, D-Wave has succeeded in pushing this boundary to the realm of industrial use. Furthermore, they have recently opened up to mixed integer linear programming (MILP) problems, expanding their applicability to many relevant problems in the field of optimisation. However, the extent of their suitability for diverse problem categories and their computational advantages remains unclear. This study conducts a comprehensive examination by applying a selection of diverse case studies to benchmark the performance of D-Wave’s hybrid solver against that of industry-leading solvers such as CPLEX, Gurobi, and IPOPT. The findings indicate that D-Wave’s hybrid solver is currently most advantageous for integer quadratic objective functions and shows potential for quadratic constraints. To illustrate this, we applied it to a real-world energy problem, specifically the MILP unit commitment problem. While D-Wave can solve such problems, its performance has not yet matched that of its classical counterparts.

Authors: Finley Alexander Quinton, Per Arne Sevle Myhr, Mostafa Barani, Pedro Crespo del Granado & Hongyu Zhang 

Link to manuscript: https://www.nature.com/articles/s41598-025-96220-2

Abstract

Uncertainty is fundamental in modern power systems, where renewable generation and fluctuating demand make stochastic optimization indispensable. The chance constrained unit commitment problem (UCP) captures this uncertainty but rapidly becomes computationally challenging as the number of scenarios grows. Quantum computing has been proposed as a potential route to overcome such scaling barriers. In this work, we evaluate the applicability of quantum annealing platforms to the chance constrained UCP. Focusing on a scenario approximation, we reformulated the problem as a mixed integer linear program and solved it using DWave hybrid quantum classical solver alongside Gurobi. The hybrid solver proved competitive under strict runtime limits for large scenario sets (15,000 in our experiments), while Gurobi remained superior on smaller cases. QUBO reformulations were also tested, but current annealers cannot accommodate stochastic UCPs due to hardware limits, and deterministic cases suffered from embedding overhead. Our study delineates where chance constrained UCPs can already be addressed with hybrid quantum classical methods, and where current quantum annealers remain fundamentally limited.

Authors: David Ribes, Tatiana Gonzalez Grandon

Link to preprint: https://arxiv.org/abs/2512.03925