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A Quantitative Fitness Analysis Workflow
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Hybrid quantum search with genetic algorithm optimization.

Sebastian Mihai Ardelean1, Mihai Udrescu1

  • 1Department of Computer and Information Technology, University Politehnica of Timisoara, Timisoara, Timis, Romania.

Peerj. Computer Science
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) to improve quantum search efficiency. HQAGO reduces the complexity of quantum genetic algorithms, making them more practical for complex optimization tasks.

Keywords:
Genetic algorithm optimizationHybrid quantum genetic algorithmQuantum computingQuantum genetic algorithms

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Quantum genetic algorithms (QGA) combine quantum computing and genetic programming for optimization.
  • Existing QGA methods often add quantum resources to classical algorithms, with unclear benefits.
  • Reduced Quantum Genetic Algorithm (RQGA) uses Grover's algorithm but has exponential runtime complexity.

Purpose of the Study:

  • To address the complexity issue in RQGA by introducing a novel optimization strategy.
  • To develop a more efficient quantum genetic algorithm for search and optimization problems.
  • To improve the performance and reduce the runtime complexity of RQGA.

Main Methods:

  • Introduced a classical optimization strategy for a fully quantum algorithm (RQGA).
  • Controlled RQGA complexity by fixing a subset of qubits as classical values using a genetic algorithm.
  • Improved RQGA performance by discarding unfit solutions and bounding the search space.

Main Results:

  • Developed the Hybrid Quantum Algorithm with Genetic Optimization (HQAGO).
  • Reduced the oracle query complexity of RQGA from O(2^n/2) to O(2^(n-k)/2).
  • Demonstrated a method for classical optimization of fully quantum algorithms.

Conclusions:

  • HQAGO offers a more efficient approach to quantum search and optimization problems.
  • The novel strategy effectively reduces the complexity of quantum genetic algorithms.
  • This work presents a promising direction for practical quantum algorithm development.