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 publication: 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. In stochastic optimization, chance constraints enable the representation of uncertainty in renewable energy sources that affect the dispatch and commitment of bulk generation. This is commonly known as the chance-constrained unit commitment problem (UCP), which 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 reformulate the problem as a mixed-integer linear program (MILP) and solve it using D-Wave’s hybrid quantum–classical solver alongside the classical solver Gurobi. The hybrid solver proves competitive under strict runtime limits for large scenario sets (15,000 in our experiments), while Gurobi remains superior on smaller cases. QUBO reformulations have also been tested, but current annealers cannot accommodate stochastic UCPs due to hardware limits, and deterministic cases suffer 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 Marzá, Tatiana González Grandón

Link to publication: https://link.springer.com/article/10.1186/s13362-026-00181-8

Abstract

Current power and energy systems are vastly described by Mixed-Integer Linear Programs (MILPs), a type of optimization problem involving both binary and continuous variables. Such problems fall into the NP-hard complexity class, which cannot be efficiently tackled by classical optimizers. In the last decades, quantum computers have emerged and have been shown to demonstrate potential for certain NP-hard problems, particularly those involving binary decision variables. Thus, it is reasonable to consider splitting mixed-integer programs into a continuous and a binary problem, solved with classical solver and a quantum computer respectively. In this work we apply Benders decomposition to solve a Unit Commitment Problem (UCP) and employ quantum and quantum-inspired solvers to solve the binary master problem. From our results, we show that generally Digital Annealing (DA) outperforms Simulated Annealing (SA) likely due to using the parallel tempering method. Moreover, we demonstrate that increasing the number of iterations or sweeps employed to solve the master problem does not necessarily increase the performance of the whole Benders decomposition algorithm.

Authors: Henrik Idsal, David Ribes Marzá, Mostafa Barani and Pedro Crespo del Granado