Traveling Salesman Problem solution using Magnonic Combinatorial Device

TL;DR

This paper introduces a novel magnonic combinatorial device designed to solve the Traveling Salesman Problem by leveraging wave superposition and parallel propagation, offering a hardware-based alternative to traditional digital algorithms.

Contribution

It presents a new hardware approach using magnonic devices with magnetic and electric components to solve TSP efficiently through wave superposition and amplification.

Findings

Numerical modeling demonstrates TSP solutions for four and six cities.

Experimental data confirms the device's capability with four cities.

The device operates based on classical wave superposition principles.

Abstract

Traveling Salesman Problem (TSP) is a decision-making problem that is essential for a number of practical applications. Today, this problem is solved on digital computers exploiting Boolean-type architecture by checking one by one a number of possible routes. In this work, we describe a special type of hardware for the TSP solution. It is a magnonic combinatorial device comprising magnetic and electric parts connected in the active ring circuit. There is a number of possible propagation routes in the magnetic mesh made of phase shifters, frequency filters, and attenuators. The phase shifters mimic cities in TSP while the distance between the cities is encoded in the signal attenuation. The set of frequency filters makes the waves on different frequencies propagate through the different routes. The principle of operation is based on the classical wave superposition. There is a number of waves coming in all possible routes in parallel accumulating different phase shifts and amplitude damping. However, only the wave(s) that accumulates the certain phase shift will be amplified by the electric part. The amplification comes first to the waves that possess the minimum propagation losses. It makes this type of device suitable for TSP solution, where waves are similar to the salesmen traveling in all possible routes at a time. We present the results of numerical modeling illustrating the TSP solutions for four and six cities. Also, we present experimental data for the TSP solution with four cities.