Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2610.14062

Grover Search for a Threshold-Fraction Variant of the Travelling Salesman Problem: A Circuit-Level Construction

Aayra Dhindsa Sawkmie

cs.DSquant-phSubmitted Oct 7, 2026• Announced Oct 8, 2026• 0 citations
arXivHTMLPDF

Abstract

The Travelling Salesman Problem (TSP) is a classic combinatorial optimisation problem which is NP-hard in optimisation form and NP-complete as a decision problem. The standard question asks for the shortest tour that visits every city exactly once and returns to the origin. This paper studies a threshold-fraction variant, which we call the robust TSP, formulated as a promise problem: given a distance threshold $K$ and a promised fraction $p$, decide whether at least a fraction $p$ of all tours have total length at most $K$, or whether no such tour exists. We show that Grover's quantum search algorithm decides this problem using $O(1/\sqrt{p})$ oracle queries, independent of the number of cities $N$, with each query implemented by a quantum circuit of polynomial size in $N$. For any fixed robustness level $p$, the total runtime is therefore polynomial in $N$. Measured in oracle queries, the quantum algorithm offers a quadratic improvement in the dependence on $1/p$ over classical random sampling, which needs $Θ(1/p)$ samples; because each quantum query is costly, however, it is faster in total running time only when $p$ is very small (roughly below $N^{-4}$), where the number of queries is no longer constant. We describe the problem formulation, detail the oracle construction (edge-length loading, sequential quantum addition, and threshold comparison), and analyse the query complexity to achieve the required result.

Comments
12 pages, 2 figures

Related papers

Travelling Salesman Problem in the Classical to Quantum Era: A Comparative Review of Algorithms and Computational Complexity

Istiyak Amin Santo, Siow Chun Lim, Md Serajun Nabi +3 more

The Travelling Salesman Problem (TSP) asks for the shortest tour that visits each city once and returns to the start. It is a classic NP-hard problem and a benchmark for optimization, logistics, and network design. This paper surveys classical, quantum, and hybrid algorithms for the TSP using clear, step-by-step explanations. We summarize how each method works, what it guarantees, and how it scales with the number of cities. We also discuss practical limits of current quantum hardware and outline open problems and promising research directions. While quantum techniques can speed up certain subroutines or offer strong heuristics, no known method solves general TSP in polynomial time. Our goal is to offer a readable yet rigorous map of the landscape for students and practitioners.

•Nov 12, 2025•1 citation

Quantum Algorithm for the Traveling Salesperson Problem

Andrew Haverly, M. A. Novotny, Shahram Rahimi

The traveling salesperson problem (TSP) is “if a traveling salesman wishes to visit exactly once each of a list of$m$cities (where the cost of traveling from city$i$to city$j$is$c_{ij}$) and then return to the home city, what is the least costly route the traveling salesman can take [1]?” The TSP is an NP-Complete problem. This paper introduces a solution to the TSP using Grover's quantum algorithm. This paper also introduces a quantum algorithm to the minimum (and maximum) spanning tree problem. The full circuit implementation is presented using the open-source Qiskit environment [2]. This paper provides the number of qubits required, the quantum depth, and the quantum volume of the algorithm for the TSP. This paper also shows how to solve the minimum (and maximum) spanning tree using Grover's algorithm.

IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)•Dec 4, 2023•1 citation

Quantum iterative approach to the Traveling Salesman Problem

Arturo Rodríguez-Almazán, Guillermo Rivas, Ricardo S. Alonso +2 more

The Traveling Salesman Problem (TSP) is a classical NP-hard problem in combinatorial optimization, where determining the shortest route among a set of cities becomes computationally prohibitive as the problem size increases. This work explores quantum computing as an alternative approach to address this complexity. Unlike existing methods that primarily rely on quantum annealing, we propose a quantum iterative framework integrating Quantum Phase Estimation (QPE) and Grover's search algorithm. Route costs are encoded as quantum phases, enabling QPE to efficiently evaluate them, while Amplitude Amplification, implemented via the Grover-Long algorithm, iteratively refines the solution space toward the optimal route. A proof-of-concept case study on a small-scale TSP instance demonstrates the feasibility of this approach and its potential for scaling to larger optimization problems. Furthermore, under an expectation-based analysis, the algorithm exhibits an expected computational complexity of $O(\frac{m^2\log_2(m)\log_2(1/ε)}{\sqrtε})$ which depends on the error tolerance parameter $ε$. This estimation omits the initialization term, which we expect future refinements to render subdominant to Phase Estimation.

quant-pharXiv:2606.11843•Physica Scripta•Jun 11, 2026•0 citations

An Efficient Quantum Algorithm for the Traveling Salesman Problem

Anant Sharma, Nupur Deshpande, Sanchita Purohit Ghosh +2 more

Abstract The traveling salesman problem is the problem of finding out the shortest route in a network of cities, that a salesman needs to travel to cover all the cities, without visiting the same city more than once. This problem is known to be N P -hard with a brute-force complexity of O ( N N ) or O ( N 2 N ) for N number of cities. This problem is equivalent to finding out the shortest Hamiltonian cycle in a given graph, if at least one Hamiltonian cycle exists in it. Quantum algorithms for this problem typically provide with a quadratic speedup only, using Grover’s search, thereby having a complexity of O ( N N /2 ) or O ( N N ). We present a bounded-error quantum polynomial-time (BQP) algorithm for solving the problem, providing with an exponential speedup. The overall complexity of our algorithm is O ( N 3 log( N ) κ / ϵ + 1/ ϵ 3 ), where the errors ϵ are O (1/poly( N )), and κ is the not-too-large condition number of the matrix encoding all Hamiltonian cycles.

Quantum Information and Computation•Feb 22, 2025•1 citation

Quantum Divide-and-Conquer for the Traveling Salesman Problem: Surpassing the $2^n$ Barrier

Xujun Bai, Yun Shang, Honghong Lin

The traveling salesman problem (TSP) is a classic NP-hard problem. Held--Karp dynamic programming~\cite{held1962dynamic, bellman1962dynamic} solves it exactly in $O(n^2 2^n)$ time, a barrier that has stood for over six decades. Whether quantum computing can surpass $O^*(2^n)$ is a central open question. The authors of~\cite{ambainis2019quantum}\ (SODA~2019) claimed a query complexity of $O^*(1.727^n)$, but we identify a structural counting error: when corrected, their scheme requires $Ω^*(2^n)$ queries and offers no advantage over classical Held--Karp. We design a quantum divide-and-conquer framework: partition $n$-vertex set into $k$ subsets, classically precompute shortest paths within each, then search over all $k$-partitions via quantum minimum finding. We prove $k=3$ achieves $O^*(1.890^n)$, and $k=4$ attains the global optimum $O^*(1.866^n)$, the first quantum algorithm to surpass $O^*(2^n)$ for general TSP. To convert the query bound into a time-complexity advantage, we overcome the oracle-construction bottleneck by preparing a set partition state, requiring $O(n^2)$ gates and $O(n)$ depth. Leveraging structured state preparation, we achieve a total time complexity of $O^*(1.866^n)$. Qiskit simulations on $n=6,7$ achieve $98.9\%$ and $100\%$ accuracy.

quant-pharXiv:2606.07322•Jun 8, 2026•0 citations

Efficient quantum algorithm for solving travelling salesman problem: An IBM quantum experience

Karthik Srinivasan, Saipriya Satyajit, Bikash K. Behera +1 more

The famous Travelling Salesman Problem (TSP) is an important category of optimization problems that is mostly encountered in various areas of science and engineering. Studying optimization problems motivates to develop advanced techniques more suited to contemporary practical problems. Among those, especially the NP hard problems provide an apt platform to demonstrate supremacy of quantum over classical technologies in terms of resources and time. TSP is one such NP hard problem in combinatorial optimization which takes exponential time order for solving by brute force method. Here we propose a quantum algorithm to solve the travelling salesman problem using phase estimation technique. We approach the problem by encoding the given distances between the cities as phases. We construct unitary operators whose eigenvectors are the computational basis states and eigenvalues are various combinations of these phases. Then we apply phase estimation algorithm to certain eigenstates which give us all the total distances possible for all the routes. After obtaining the distances we can search through this information using the quantum search algorithm for finding the minimum to find the least possible distance as well the route taken. This provides us a quadratic speedup over the classical brute force method for a large number of cities. In this paper, we illustrate an example of the travelling salesman problem by taking four cities and present the results by simulating the codes in the IBM's quantum simulator.

arXiv:1805.10928•May 28, 2018•62 citations
The Travelling Salesman Problem (TSP) is a classic combinatorial optimisation problem which is NP-hard in optimisation form and NP-complete as a decision problem. The standard question asks for the shortest tour that visits every city exactly once and returns to the origin. This paper studies a threshold-fraction variant, which we call the robust TSP, formulated as a promise problem: given a distance threshold KKK and a promised fraction ppp, decide whether at least a fraction ppp of all tours have total length at most KKK, or whether no such tour exists. We show that Grover's quantum search algorithm decides this problem using O(1/p)O(1/\sqrt{p})O(1/p​) oracle queries, independent of the number of cities NNN, with each query implemented by a quantum circuit of polynomial size in NNN. For any fixed robustness level ppp, the total runtime is therefore polynomial in NNN. Measured in oracle queries, the quantum algorithm offers a quadratic improvement in the dependence on 1/p1/p1/p over classical random sampling, which needs Θ(1/p)Θ(1/p)Θ(1/p) samples; because each quantum query is costly, however, it is faster in total running time only when ppp is very small (roughly below N−4N^{-4}N−4), where the number of queries is no longer constant. We describe the problem formulation, detail the oracle construction (edge-length loading, sequential quantum addition, and threshold comparison), and analyse the query complexity to achieve the required result.
The Travelling Salesman Problem (TSP) asks for the shortest tour that visits each city once and returns to the start. It is a classic NP-hard problem and a benchmark for optimization, logistics, and network design. This paper surveys classical, quantum, and hybrid algorithms for the TSP using clear, step-by-step explanations. We summarize how each method works, what it guarantees, and how it scales with the number of cities. We also discuss practical limits of current quantum hardware and outline open problems and promising research directions. While quantum techniques can speed up certain subroutines or offer strong heuristics, no known method solves general TSP in polynomial time. Our goal is to offer a readable yet rigorous map of the landscape for students and practitioners.
The traveling salesperson problem (TSP) is “if a traveling salesman wishes to visit exactly once each of a list ofmmmcities (where the cost of traveling from cityiiito cityjjjiscijc_{ij}cij​) and then return to the home city, what is the least costly route the traveling salesman can take [1]?” The TSP is an NP-Complete problem. This paper introduces a solution to the TSP using Grover's quantum algorithm. This paper also introduces a quantum algorithm to the minimum (and maximum) spanning tree problem. The full circuit implementation is presented using the open-source Qiskit environment [2]. This paper provides the number of qubits required, the quantum depth, and the quantum volume of the algorithm for the TSP. This paper also shows how to solve the minimum (and maximum) spanning tree using Grover's algorithm.
The Traveling Salesman Problem (TSP) is a classical NP-hard problem in combinatorial optimization, where determining the shortest route among a set of cities becomes computationally prohibitive as the problem size increases. This work explores quantum computing as an alternative approach to address this complexity. Unlike existing methods that primarily rely on quantum annealing, we propose a quantum iterative framework integrating Quantum Phase Estimation (QPE) and Grover's search algorithm. Route costs are encoded as quantum phases, enabling QPE to efficiently evaluate them, while Amplitude Amplification, implemented via the Grover-Long algorithm, iteratively refines the solution space toward the optimal route. A proof-of-concept case study on a small-scale TSP instance demonstrates the feasibility of this approach and its potential for scaling to larger optimization problems. Furthermore, under an expectation-based analysis, the algorithm exhibits an expected computational complexity of O(m2log⁡2(m)log⁡2(1/ε)ε)O(\frac{m^2\log_2(m)\log_2(1/ε)}{\sqrtε})O(ε​m2log2​(m)log2​(1/ε)​) which depends on the error tolerance parameter εεε. This estimation omits the initialization term, which we expect future refinements to render subdominant to Phase Estimation.
Abstract The traveling salesman problem is the problem of finding out the shortest route in a network of cities, that a salesman needs to travel to cover all the cities, without visiting the same city more than once. This problem is known to be N P -hard with a brute-force complexity of O ( N N ) or O ( N 2 N ) for N number of cities. This problem is equivalent to finding out the shortest Hamiltonian cycle in a given graph, if at least one Hamiltonian cycle exists in it. Quantum algorithms for this problem typically provide with a quadratic speedup only, using Grover’s search, thereby having a complexity of O ( N N /2 ) or O ( N N ). We present a bounded-error quantum polynomial-time (BQP) algorithm for solving the problem, providing with an exponential speedup. The overall complexity of our algorithm is O ( N 3 log( N ) κ / ϵ + 1/ ϵ 3 ), where the errors ϵ are O (1/poly( N )), and κ is the not-too-large condition number of the matrix encoding all Hamiltonian cycles.
Quantum Divide-and-Conquer for the Traveling Salesman Problem: Surpassing the 2n2^n2n Barrier
The traveling salesman problem (TSP) is a classic NP-hard problem. Held--Karp dynamic programming~\cite{held1962dynamic, bellman1962dynamic} solves it exactly in O(n22n)O(n^2 2^n)O(n22n) time, a barrier that has stood for over six decades. Whether quantum computing can surpass O∗(2n)O^*(2^n)O∗(2n) is a central open question. The authors of~\cite{ambainis2019quantum}\ (SODA~2019) claimed a query complexity of O∗(1.727n)O^*(1.727^n)O∗(1.727n), but we identify a structural counting error: when corrected, their scheme requires Ω∗(2n)Ω^*(2^n)Ω∗(2n) queries and offers no advantage over classical Held--Karp. We design a quantum divide-and-conquer framework: partition nnn-vertex set into kkk subsets, classically precompute shortest paths within each, then search over all kkk-partitions via quantum minimum finding. We prove k=3k=3k=3 achieves O∗(1.890n)O^*(1.890^n)O∗(1.890n), and k=4k=4k=4 attains the global optimum O∗(1.866n)O^*(1.866^n)O∗(1.866n), the first quantum algorithm to surpass O∗(2n)O^*(2^n)O∗(2n) for general TSP. To convert the query bound into a time-complexity advantage, we overcome the oracle-construction bottleneck by preparing a set partition state, requiring O(n2)O(n^2)O(n2) gates and O(n)O(n)O(n) depth. Leveraging structured state preparation, we achieve a total time complexity of O∗(1.866n)O^*(1.866^n)O∗(1.866n). Qiskit simulations on n=6,7n=6,7n=6,7 achieve 98.9%98.9\%98.9% and 100%100\%100% accuracy.
The famous Travelling Salesman Problem (TSP) is an important category of optimization problems that is mostly encountered in various areas of science and engineering. Studying optimization problems motivates to develop advanced techniques more suited to contemporary practical problems. Among those, especially the NP hard problems provide an apt platform to demonstrate supremacy of quantum over classical technologies in terms of resources and time. TSP is one such NP hard problem in combinatorial optimization which takes exponential time order for solving by brute force method. Here we propose a quantum algorithm to solve the travelling salesman problem using phase estimation technique. We approach the problem by encoding the given distances between the cities as phases. We construct unitary operators whose eigenvectors are the computational basis states and eigenvalues are various combinations of these phases. Then we apply phase estimation algorithm to certain eigenstates which give us all the total distances possible for all the routes. After obtaining the distances we can search through this information using the quantum search algorithm for finding the minimum to find the least possible distance as well the route taken. This provides us a quadratic speedup over the classical brute force method for a large number of cities. In this paper, we illustrate an example of the travelling salesman problem by taking four cities and present the results by simulating the codes in the IBM's quantum simulator.