Sep 30, 2021 · This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm (QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developedfor the indexing and unindexing of the solution space and for implementing the required alternatingphase-walk unitaries, which are …

Mar 28, 2023 · In order to reduce the search space and computational complexity of an EA we suggest a two phased evolutionary approach based on the Quantum Whale Optimization Algorithm (QWOA). In the first phase, QWOA is used to reduce and select the optimum combination of feature extraction methods.

Oct 1, 2021 · One candidate is the Quantum Walk-based Optimisation Algorithm (QWOA) [2,3]. This algorithm makes use of alternating quality-dependent phase-shifts and continuous-time quantum walks over a circulant graph connecting all possible solutions in the solutions space.

Jun 8, 2020 · We present a highly efficient quantum circuit for performing continuous time quantum walks (CTQWs) over an exponentially large set of combinatorial objects, provided that the objects can be indexed efficiently.

Dec 7, 2024 · QWOA is a quantum wavefunction-based metaheuristic optimization algorithm used to determine the optimal cost for the TSP. This algorithm is entirely inspired by the properties of wavefunctions exhibited by quantum particles.

Apr 1, 2020 · In this paper, we propose the Quantum Whale Optimization Algorithm (QWOA) for feature selection, which is an amalgamation of the Quantum Concepts and the Whale Optimization Algorithm (WOA).

Click to begin playing. Use the QWOP keys to move your legs, but remember, it's not about whether you win or lose.

In terms of the QWOA this implies potential degradation of the quantum walk, causing leakage of amplitude to infeasible solutions. Here, we present an improved highly efficient quantum circuit for fast-forwarded quantum walk simulation on complete graphs connecting the feasible solutions.

Apr 1, 2020 · A quantum-based approach of the classical WOA, QWOA, is proposed that further enhances the diversification and convergence properties of this swarm based wrapper feature selection method, through the quantum-bit representation of the population.

在本文中，我们提出了用于特征选择的量子鲸鱼优化算法（qwoa），这是量子概念和鲸鱼优化算法（woa）的合并。 所提出的方法通过使用总体个体的量子位表示和量子旋转门算子作为变异算子来增强经典WOA的探索和开发能力。