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02 QAOA - work in progress.
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‎tutorials/01_Introduction_to_TSP.ipynb‎

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"For now, we will use only classical algorithms - we will go quantum in the next tutorial."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this tutorial you will learn the following:\n",
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"- What is the Travelling Salesman Problem?\n",
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"- Why it's important?\n",
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"- How can we solve it?"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"What we covered?\n",
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"What we learned?\n",
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"\n",
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"- TSP is an optimization problem, where we want to find the shortest route which allows us to visit all the cities\n",
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"- TSP has many variations and formulations, many applications and can be solved in many ways\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Additional sources"
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"## Additional resources"
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‎tutorials/02_QAOA.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# QAOA - Quantum Approximate Optimization Algorithm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this notebook I would like to cover one of the most prominent algorithms in the quantum computing in the last few years."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this tutorial you will learn the following:\n",
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"- How to use pyQuil - python platform for quantum computing?\n",
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"- What is QAOA?\n",
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"TODO"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before we can go into QAOA itself, you should prepare a couple of things:\n",
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"1. Configure your Forest API Key - go to http://forest.rigetti.com, find section \"Get API Key\" and follow the instructions\n",
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"2. Install [pyQuil](https://github.com/rigetticomputing/pyquil) and [Grove](https://github.com/rigetticomputing/grove)\n",
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"3. Learn the basics of quantum computing by yourself - there are plenty of great resources about the basics in the web and I decided I don't want to reinvent the wheel in this tutorial. I recommend going to [pyQuil documentation](http://pyquil.readthedocs.io/en/stable/).\n",
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"\n",
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"Even though I tried to make this tutorial as accessible to the not-quantum-computing people as possible, you probably need some level of familiarity with the following concepts:\n",
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"- qubits\n",
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"- quantum gates\n",
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"- superposition\n",
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"- entanglement\n",
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"\n",
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"You don't need to go very in-depth - if something is confusing you can always come back to the documentation when you need it."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## QAOA - intro"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"QAOA is an algorithm for solving a broad range of optimization problems using NISQ (Noisy Intermediate-Scale Quantum) devices. \n",
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"\n",
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"Let's start easy and instead of going straight to the TSP we will solve MaxCut problem."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### MaxCut - explanation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the MaxCut problem we start with a graph and we want to divide it into two subgraphs in such way, that the edges between them would have the higher possible sum."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#TODO Picture"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### QAOA solves MaxCut"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will use a class maxcut_solver which we can find in the grove library."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa\n",
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"import pyquil.api as api\n",
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"qvm_connection = api.QVMConnection()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## QAOA - how does it work?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Additional resources"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- https://arxiv.org/pdf/1801.00862.pdf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "qenv",
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"language": "python",
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"name": "qenv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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