Solving The Travelling Salesman Problem Using A Single Qubit

TL;DR

This paper introduces a novel quantum algorithm that solves the Traveling Salesman Problem using a single qubit, leveraging quantum parallelism and optimal control to efficiently find the shortest route, outperforming existing quantum methods for small instances.

Contribution

The paper presents a single-qubit quantum algorithm for TSP that reduces resource requirements and improves accuracy compared to prior quantum approaches, with potential scalability.

Findings

Successfully solves TSP for 4-9 cities with exact solutions.

More resource-efficient and accurate than existing quantum algorithms.

Potential polynomial speed-up over classical algorithms.

Abstract

The travelling salesman problem (TSP) is a popular NP-hard-combinatorial optimization problem that requires finding the optimal way for a salesman to travel through different cities once and return to the initial city. The existing methods of solving TSPs on quantum systems are either gate-based or binary variable-based encoding. Both approaches are resource-expensive in terms of the number of qubits while performing worse compared to existing classical algorithms even for small-size problems. We present an algorithm that solves an arbitrary TSP using a single qubit by invoking the principle of quantum parallelism. The cities are represented as quantum states on the Bloch sphere while the preparation of superposition states allows us to traverse multiple paths at once. The underlying framework of our algorithm is a quantum version of the classical Brachistochrone approach. Optimal control methods are employed to create a selective superposition of the quantum states to find the shortest route of a given TSP. The numerical simulations solve a sample of four to nine cities for which exact solutions are obtained. The algorithm can be implemented on any quantum platform capable of efficiently rotating a qubit and allowing state tomography measurements. For the TSP problem sizes considered in this work, our algorithm is more resource-efficient and accurate than existing quantum algorithms with the potential for scalability. A potential speed-up of polynomial time over classical algorithms is discussed.