Optimization problems are often highly constrained and evolutionary algorithms (EAs) are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA) for solving constr...
Quantum annealing (QA)1,2, which is a quantum heuristic algorithm for solving combinatorial optimization problems, has attracted a great deal of attention because it is implemented using real quantum systems by D-Wave Systems Inc.3,4, aiming at becoming more powerful than classical algorithms such...
This class is selected by its well applicability in the field of quantum chemistry and combinatorial problems. The work also studied the application of quasi-Newton optimization methods in hybrid algorithms. 5.2 Computational problems In [273], the authors studied the quantum gradient descent for ...
Among several quantum algorithms implemented on noisy intermediate-scale quantum (NISQ) devices1,2,3,4,5,6,7,8,9,10,11,12, the quantum approximate optimization algorithm (QAOA) offers an opportunity to approximately solve combinatorial optimization problems such as MaxCut, Max Independent Set, and...
Quantum Algorithms for Optimization Problems Quantum computing has the potential to revolutionize optimization problems by providing exponential speedups compared to classical algorithms. In this markdown, we will explore how quantum algorithms can be used to solve optimization problems, specifically the Trave...
They are quantum physicists who have these wacky algorithms that are way better,’” he said.To solve optimization problems, computers look for a solution that requires the lowest amount of effort or cost. In some cases, though, that’s like a mountain climber who’s trying to find the ...
Theqiskit.optimizationpackage covers the whole range from high-level modeling of optimization problems, with automatic conversion of problems to different required representations, to a suite of easy-to-use quantum optimization algorithms that are ready to run on classical simulators, as well as on re...
In addition, we have also taken up domain-oriented computing specialized for applications and developed and released the Digital Annealer, which is specialized for optimization problems. Computers that utilize quantum phenomena include gate-based quantum computers and quantum Ising machines. The Digital ...
Quantum computers provide a valuable resource to solve computational problems. The maximization of the objective function of a computational problem is a crucial problem in gate-model quantum computers. The objective function estimation is a high-cost pr
Since the origins of quantum genetic algorithms (QGAs) [9] until today, lots of QGAs have been proposed in the scientific literature [37,38,39,40,41]. All kinds of quantum evolutionary algorithms have been successfully applied to optimization problems, such as the personnel scheduling problem ...