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March 25, 2026Open Access

Quantum Computing For Combinatorial Optimization: Algorithms, Complexity Analysis, And Real-World Applications.

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Authors

GSG. SwapnaPSP. Sunil

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Implication

Investigates quantum algorithms for solving combinatorial optimization in various fields, suggesting future implications.

Key Points

  • The research aims to explore the effectiveness of quantum computing in solving NP-hard combinatorial optimization problems.
  • Analyzed combinatorial optimization problems like Max-Cut and the Knapsack problem.
  • Employed Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm.
  • Evaluated algorithm performance using metrics such as accuracy, execution time, and approximation ratio.
  • Compared quantum algorithm results with classical optimization techniques.
  • QAOA achieved accuracy levels of up to 93% for small problem instances.
  • Classical methods were more efficient for small-scale problems.
  • Quantum algorithms showed significant potential for scalability in complex optimization tasks.

Cite This Study

Swapna et al. (2026) studied this question.

synapsesocial.com/papers/69c37b81b34aaaeb1a67df74https://doi.org/10.5281/zenodo.19184381
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