Towards Implementable Quantum Divide and Conquer: A TSP Solver with Improved Exponential Base over Held-Karp

Abstract:The traveling salesman problem (TSP) is a significant classical NP-hard combinatorial optimization problem. In this work, we demonstrate that combining classical dynamic programming with quantum search can yield an achievable quantum advantage for TSP on the basis of excellent work by the authors of~\cite{ambainis2019quantum}. We design the quantum divide and conquer strategy to provide a parameterized spectrum for this combination. The hybrid algorithm proposed in~\cite{ambainis2019quantum} corresponds to a specific case in this spectrum, while the two extremes of the spectrum represent the purely classical Held-Karp and the purely quantum search algorithm, respectively. Within our parameterized spectrum, we prove that the optimal query complexity is $O^*(1.865666\ldots^n)$, achieved with the 4-subset scheme, while the counting in~\cite{ambainis2019quantum} overlooked half of the recursive branches. The correct query complexity of their algorithm is $O^*(2.225880\ldots^n)$ at their chosen parameter ($\alpha\approx0.055362$), and cannot fall below $O^*(2^n)$ for any $\alpha$ - meaning their $8$-subset scheme, correctly analyzed, never surpasses the classical Held-Karp bound. Furthermore, in previous studies on quantum advantages for NP-hard combinatorial optimization problems, researchers focused only on improvements in query complexity. Our work, however, points out that the quantum advantage stems not only from the quadratic speedup of quantum search but also from the structured quantum state preparation. We argue that structured state preparation is indispensable for realizing the oracle operator while maintaining the total time complexity of $O^*(1.865666\ldots^n)$. Therefore, we design an elegant method for preparing the set partition state, which makes our TSP solver practically executable.

Quantum Physics (quant-ph); Computational Complexity (cs.CC)

arXiv:2606.07322 [quant-ph]

(or arXiv:2606.07322v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.07322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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