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quantum_tsp_tutorials

Maintained by mstechly

This repository contains code with tutorials of how to approach optimization problems using quantum computing.

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What you can build or learn

Get a gentler introduction before diving into full SDKs.
See how different projects teach circuits, superposition, and measurement.
Identify resources that work well for onboarding or self-study.

Code samples

Examples from the repository.

01 Introduction To Tsp (tutorials/01_Introduction_to_TSP.ipynb)

def create_cities(N):
    """
    Creates an array of random points of size N.
    """
    cities = []
    for i in range(N):
        cities.append(np.random.rand(2) * 10)
    return np.array(cities)

02 Qaoa (tutorials/02_QAOA.ipynb)

import numpy as np
from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa
import pyquil.api as api
qvm_connection = api.QVMConnection()

03 Naive Approach (tutorials/03_Naive_approach.ipynb)

import numpy as np
%matplotlib inline


def points_order_to_binary_state(points_order):
    """
    Transforms the order of points from the standard representation: [0, 1, 2],
    to the binary one: [1,0,0,0,1,0,0,0,1]
    """
    number_of_points = len(points_order)
    binary_state = np.zeros((len(points_order))**2)
    for j in range(len(points_order)):
        p = points_order[j]
        binary_state[(number_of_points) * (j) + (p)] = 1
    return binary_state

def binary_state_to_points_order(binary_state):
    """
    Transforms the the order of points from the binary representation: [1,0,0,0,1,0,0,0,1],
    to the binary one: [0, 1, 2]
    """
    points_order = []
    number_of_points = int(np.sqrt(len(binary_state)))
    for p in range(number_of_points):
        for j in range(number_of_points):
            if binary_state[(number_of_points) * p + j] == 1:
                points_order.append(j)
    return points_order

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Quantum TSP tutorial

Introduction

This repository contains code with tutorials on how to approach optimization problems using quantum computing. I used Travelling Salesman Problem for this tutorials, though the idea is, that after finishing it, you should be able to implement any similar optimization problem.

Is this tutorial right for you?

I have created this tutorial with specific audience in mind. It means people, who:

are curious about quantum computing,
don't have any background in quantum physics,
have some programming experience,
really want to learn this topic.

The last point is the most important. My goal here is not to show you how to solve TSP with a quantum computer. My goal is to teach you how to solve optimization problems with QC, make sure your solution works and how to improve it. To do all that you need to put some effort into it and spend a couple of hours trying to understand all the concepts. But this is on purpose - this is the best way I know to actually learn the topic and integrate this knowledge.

I did my best to provide you with all the necessary knowledge, code examples etc. If you think something is not clear, missing, you have a better idea - well, I encourage you to do one of these two things:

Do a pull request to this repository
Write me an e-mail at michal.stechly@gmail.com

I don't want to say that it is super advanced and hard - as stated earlier, you don't have to be a quantum computing expert.

Dependencies

In this tutorial I used pyquil 2.2.1 and grove 1.7.0 . API of these librares may change in next versions - let me know if something is broken so I can fix it :)

About the author

My name is Michał and I work as Quantum Software Engineer at Zapata Computing. You can find more materials onmy blog Musty thoughts.

If you want to contact me - feel free to do so: michal.stechy@gmail.com .

Unitary Fund

This project is supported by Unitary Fund. If you have an idea for an open-source project for near term hybrid quantum-classical programming, this is a good place for you!