# Approaching graph problems with continuous variable quantum computing

## TL;DR

This paper presents a novel quantum computing method using continuous variables and variational algorithms to solve the Max-Cut problem, analyzing the impact of non-Gaussian gates and framing the circuit as a machine learning model.

## Contribution

It introduces a new continuous-variable quantum approach for Max-Cut, combining graph embedding and variational circuits, with analysis of non-Gaussian gate effects.

## Key findings

- Non-Gaussian gates influence optimization performance
- Proposed a machine learning perspective for quantum circuits
- Numerical simulations demonstrate potential effectiveness

## Abstract

We introduce a method for solving the Max-Cut problem using a variational algorithm and a continuous-variables quantum computing approach. The quantum circuit consists of two parts: the first one embeds a graph into a circuit using the Takagi decomposition and the second is a variational circuit which solves the Max-Cut problem. We analyze how the presence of different types of non-Gaussian gates influences the optimization process by performing numerical simulations. We also propose how to treat the circuit as a machine learning model.