BranchesTags

Latest commit

History

39 Commits

Folders and files

NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md

Repository files navigation

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:

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!

Unitary Fund

Thanks

Thanks to Jacek Łysiak and Katerina Gratsea for feedback!

About

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

Resources

Readme
License
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Latest commit
History
39 Commits
Folders and files
NameName
Last commit message
Last commit date
tutorials
tutorials
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
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:
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!
Unitary Fund
Thanks
Thanks to Jacek Łysiak and Katerina Gratsea for feedback!
About
This repository contains code with tutorials of how to approach optimization problems using quantum computing.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages