Efficient quantum algorithm for solving travelling salesman problem: An IBM quantum experience
Karthik Srinivasan, Saipriya Satyajit, Bikash K. Behera, Prasanta, K. Panigrahi

TL;DR
This paper presents a quantum algorithm leveraging phase estimation and quantum search to solve the Travelling Salesman Problem more efficiently, demonstrating a quadratic speedup over classical brute force methods through simulation on IBM quantum hardware.
Contribution
The paper introduces a novel quantum algorithm for TSP that encodes distances as phases and combines phase estimation with quantum search to improve efficiency.
Findings
Quadratic speedup over classical brute force for TSP
Successful simulation of the algorithm on IBM quantum simulator
Effective encoding of city distances as quantum phases
Abstract
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
