Evolutionary Computing to Solve Optimal Entangle States in Quantum Circuits

EasyChair Preprint 10135

Abstract

This study investigates the efficacy of bio-inspired evolutionary algorithms for designing quantum circuits that proficiently generate highly entangled quantum states, a crucial prerequisite for quantum computing. By employing an evolutionary algorithm, quantum circuits are optimized for entanglement generation, with the Meyer-Wallach entanglement measure serving as the fitness function. The research highlights that an optimal mutation rate, balancing exploration and exploitation, can effectively augment the entanglement capabilities of three-, four-, and five-qubit quantum circuits. Additionally, the study unveils that increasing the number of gates in the quantum circuit inversely affects its entanglement capability. These findings offer valuable insights into the trade-off between circuit complexity and performance, bearing significant implications for the design of quantum circuits in various quantum computing applications. The outcomes of this study hold the potential to substantially contribute to the advancement of quantum computing technology.

Keyphrases: Evolutionary Algorithms, Quantum Gates, entanglement, quantum circuits

@booklet{EasyChair:10135,
  author    = {Shailendra Bhandari and Stefano Nichele and Sergiy Denysov and Pedro G. Lind},
  title     = {Evolutionary Computing to Solve Optimal Entangle States in Quantum Circuits},
  howpublished = {EasyChair Preprint 10135},
  year      = {EasyChair, 2023}}