Hybrid Quantum Genetic Algorithm for the 0-1 Knapsack Problem in the IBM Qiskit Simulator

Authors

  • Enrique Ballinas Instituto Politécnico Nacional
  • Oscar Montiel Instituto Politécnico Nacional

DOI:

https://doi.org/10.13053/cys-26-2-4253

Keywords:

Quantum computing, quantum genetic algorithm, knapsack problem

Abstract

In this work, a novel Hybrid Quantum Genetic Algorithm (HQGA) for the 0-1 Knapsack Problem (KP) is presented. It is based on quantum computing principles, such as qubits, superposition, and entanglement of states. The HQGA was simulated in the Qiskit simulator. Qiskit simulator is a platform developed by IBM that allows working with quantum computers at the level of circuits, pulses, and algorithms. The performance of HQGA is evaluated in three strongly correlated KP data sets, and computational results are compared with a Quantum-Inspired Evolutionary Algorithm (QIEA), a modified version of a QIEA (QIEA-Q), and a modified version of the HQGA (HQGA-Q). Experimental results demonstrate that the proposed HQGA can obtain the best solutions in all the KP data sets, and performs well on robustness.

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Published

2022-06-15

Issue

Vol. 26 No. 2 (2022): Emerging Issues and Applications of Fuzzy Systems, Neural Networks and Metaheuristics (Castillo, Melin, Montiel, Valdez)

Section

Articles of the Thematic Issue

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