Scholarlink

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its “Pegasus” architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, thereby reducing qubit requirements and improving efficiency. We formulate the TSP as a QUBO problem and compare quantum solutions with classical solutions for instances involving 8, 9, and 10 cities. Additionally, we compare our solver with the D-Wave TSP solver for the 20-city case. Our proposed method outperforms the D-Wave solver and achieves nearly optimal solutions. Thus, our key contribution is a critical analysis of quantum annealing’s performance and proposed enhancements to address existing limitations. This research provides insights into the strengths and weaknesses of modern quantum techniques and offers guidance for future advancements in quantum optimization.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced Quantum Annealing TSP Solver (EQATS): Advancements in Solving the Traveling Salesman Problem Using D-Wave’s Quantum Annealer

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Murhaf Alawir,
  • Mohammad Anas Alatasi,
  • Hadi Salloum,
  • Manuel Mazzara

摘要

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its “Pegasus” architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, thereby reducing qubit requirements and improving efficiency. We formulate the TSP as a QUBO problem and compare quantum solutions with classical solutions for instances involving 8, 9, and 10 cities. Additionally, we compare our solver with the D-Wave TSP solver for the 20-city case. Our proposed method outperforms the D-Wave solver and achieves nearly optimal solutions. Thus, our key contribution is a critical analysis of quantum annealing’s performance and proposed enhancements to address existing limitations. This research provides insights into the strengths and weaknesses of modern quantum techniques and offers guidance for future advancements in quantum optimization.