Анализ страницы https://quantumcomputingcourses.com/case-studies
Основное Готовность: 100%
Домен
quantumcomputingcourses.com
Состояние доменного имени
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Проверяем корректность доменного имени и наличие технических проблем на уровне домена.
Длина домена велика. Но если вы продвигаете запрос, входящий в название домена, то это хорошо.
Домен второго уровня идеален для продвижения.
Ответ сервера
200 Успешный ответ
HTTP-код ответа и цепочка редиректов
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Код 200 — страница доступна. Коды 3xx — редиректы (цепочки замедляют загрузку и размывают ссылочный вес). Коды 4xx/5xx — ошибки, поисковик не сможет проиндексировать страницу.
Сервер настроен корректно.
Безопасность
Сайт безопасен
Использование HTTPS и SSL-сертификат
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HTTPS — обязательный стандарт. Google и Яндекс отдают предпочтение защищённым сайтам. Отсутствие SSL или просроченный сертификат ведут к предупреждениям в браузере и снижению позиций.
На сайте работает защищенный протокол ssl и сайт открывается по https.
Ssl-сертификат действителен до 04.10.2026 22:45:22.
Сервер поддерживает HTTP/3 (QUIC) — новейший протокол.
Включён HSTS (Strict-Transport-Security) — защита от подмены на http.
Поздравляем! Сайт не содержится в реестре РКН.
Кодировка
UTF-8
Кодировка символов страницы
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Стандарт — UTF-8. Неправильная кодировка вызывает нечитаемые символы и мешает поисковику корректно распознать текст страницы.
Указана кодировка на странице UTF-8.
Язык
en
Атрибут lang в HTML-теге
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Атрибут lang (<html lang="ru">) сообщает поисковикам и браузерам, на каком языке написана страница. Помогает при ранжировании в региональном поиске.
Язык документа указан явно: en.
Скорость загрузки
~0,35сек
Время отклика сервера (TTFB)
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Time To First Byte — время до получения первого байта от сервера. Норма до 200 мс. Медленный отклик ухудшает пользовательский опыт и ранжирование: Яндекс и Google учитывают скорость страниц.
Скорость загрузки сайта 0,35сек оптимальна.
Объем документа
100Кб
Размер HTML-кода страницы
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Слишком большой HTML замедляет парсинг браузером и сканирование поисковым роботом. Рекомендуется не более 200 Кб.
Объем html-документа 100Кб оптимален.
Структура html-документа корректна.
Ресурсы
Ресурсы: 3
Внешние ресурсы страницы (CSS, JS, изображения)
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Количество и тип подключённых ресурсов влияют на скорость загрузки. Большое число запросов увеличивает время рендеринга страницы.
Кол-во файлов ресурсов 3 достаточно.
Показать полный список ресурсов
| Тип | Название | Значение |
|---|---|---|
| stylesheet | /_astro/about.B3FO-Cl2.css | |
| js | https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-8173927381268166 | |
| js | module | /_astro/hoisted.D0zBYRFm.js |
Серверные заголовки
Кол-во: 11
HTTP-заголовки ответа сервера
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Заголовки сервера передают браузеру и поисковику служебную информацию: кеширование, безопасность (CSP, HSTS), сжатие (gzip). Правильная настройка ускоряет загрузку и повышает защищённость.
Найдены серверные заголовки 11шт. Подробнее про серверные заголовки.
Показать полный список серверных заголовков
| Ключ | Значение |
|---|---|
| Cache-Control | public, must-revalidate, max-age=0 |
| ETag | "a03af4203d59d659b3567167e17f36185da14c80a06dafe89bf3a9a114d4d704" |
| Strict-Transport-Security | max-age=31556926 |
| Accept-Ranges | bytes |
| Date | Mon, 24 Aug 2026 20:41:04 GMT |
| X-Served-By | cache-bma-essb1270054-BMA |
| X-Cache | MISS |
| x-cache-hits | 0 |
| x-timer | S1787604064.406471,VS0,VE215 |
| Vary | x-fh-requested-host |
| Alt-Svc | h3=":443" |
CMS
Не определена
Система управления сайтом (движок)
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CMS — это движок, на котором работает сайт (WordPress, 1C-Bitrix, Tilda и др.). Знание CMS помогает понять возможности SEO-оптимизации и подобрать подходящие инструменты. «Не определена» — вероятно, самописный сайт или нестандартная сборка.
CMS не определена. Вероятно, сайт самописный либо движок надёжно скрыт. Это не ошибка.
Веб-сервер
Не определён
Программное обеспечение сервера
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Веб-сервер — это ПО, которое отдаёт страницы посетителям (nginx, Apache, IIS, LiteSpeed и др.). Определяется по серверным заголовкам ответа (Server, X-Powered-By и т.п.). «Не определён» — сервер намеренно скрывает эти заголовки, это нормальная практика безопасности.
Веб-сервер не определён — заголовок Server скрыт. Это не ошибка: часто так настраивают из соображений безопасности.
Мета-теги Готовность: 33%
Title
Quantum Computing Case Studies | QuantumComputingCourses.com
Заголовок страницы в браузере и поисковой выдаче
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Title — главный SEO-заголовок страницы. Влияет на CTR в поиске и ранжирование. Оптимальная длина: 50–70 символов. Ключевые слова — ближе к началу.
Необходимо уменьшить число символов в title (текущее значение: 60, оптимально: от 40 до 45)
Дублей словоформ в title не найдено.
Description
Real-world quantum computing case studies: Volkswagen traffic routing, Google quantum advantage, IBM battery materials, JPMorgan portfolio optimization, and…
Описание страницы в поисковой выдаче (сниппет)
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Meta Description — текст под заголовком в выдаче. Напрямую на позиции не влияет, но влияет на CTR. Оптимальная длина: 120–160 символов.
Необходимо уменьшить число символов в description (текущее значение: 157, оптимально: от 120 до 130)
Keywords
Список ключевых слов страницы (устаревший тег)
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Meta Keywords не учитывается Яндексом и Google для ранжирования с 2009–2012 годов. Заполнение не обязательно, но не вредит. Конкурент может использовать содержимое для анализа.
Установите мета-тег keywords!
Канонический Url
https://quantumcomputingcourses.com/case-studies/
Указывает поисковику основную версию страницы
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Canonical (rel=canonical) предотвращает проблему дублей страниц. Должен точно совпадать с URL проверяемой страницы. Неправильный canonical может передать ссылочный вес на другую страницу.
В каноническом Url обнаружен лишний слэш (/).
Robots
Ошибок нет
Директивы для поисковых роботов на уровне страницы
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Meta Robots управляет индексацией конкретной страницы: index/noindex — индексировать ли, follow/nofollow — следовать ли по ссылкам. Noindex полностью исключает страницу из поиска.
Meta-тег robots не указан. Страница свободна для индексации.
Адаптивность
width=device-width, initial-scale=1
Настройка масштабирования на мобильных устройствах
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Тег viewport (<meta name="viewport">) сообщает браузеру, как масштабировать страницу на мобильных. Стандарт: width=device-width, initial-scale=1. Отсутствие — признак отсутствия мобильной версии.
Meta-тег viewport со значением-константой width=device-width задаёт ширину страницы в соответствии с размером экрана.
Meta-тег viewport со значением initial-scale=1.0 определяет масштаб 1:1, т.е. «не масштабировать».
Разметка OpenGraph
Кол-во: 9
Мета-теги для красивых превью в соцсетях
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OpenGraph (og:title, og:description, og:image) управляет тем, как страница выглядит при репосте в социальных сетях и мессенджерах. Отсутствие OG-тегов — невзрачный превью при шеринге.
Разметка OpenGraph задана. Страница оптимизирована под социальные сети.
Показать полный список og мета-тегов
| Тип | Значение |
|---|---|
| og:type | website |
| og:url | https://quantumcomputingcourses.com/case-studies/ |
| og:title | Quantum Computing Case Studies | QuantumComputingCourses.com |
| og:description | Real-world quantum computing case studies: Volkswagen traffic routing, Google quantum advantage, IBM battery materials, JPMorgan portfolio optimization, and… |
| og:image | https://quantumcomputingcourses.com/og-default.png |
| og:image:width | 1200 |
| og:image:height | 630 |
| og:locale | en_US |
| og:site_name | QuantumComputingCourses.com |
Все мета-теги
Кол-во: 20
Полный список мета-тегов страницы
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Таблица всех meta-тегов, включая нестандартные. Позволяет найти опечатки, дубли и лишние теги.
Найдены мета-теги 20шт. Мета-теги не видимы для человека и предназначены для обмена информацией между веб-страницей и поисковыми системами, браузерами и другими веб-службами. С ними роботы 🤖 и устройства ведут себя более ожидаемо.
Показать полный список мета-тегов
| Тип | Название | Значение |
|---|---|---|
| name | viewport | width=device-width, initial-scale=1 |
| name | description | Real-world quantum computing case studies: Volkswagen traffic routing, Google quantum advantage, IBM battery materials, JPMorgan portfolio optimization, and… |
| name | twitter:card | summary_large_image |
| name | twitter:title | Quantum Computing Case Studies | QuantumComputingCourses.com |
| name | twitter:description | Real-world quantum computing case studies: Volkswagen traffic routing, Google quantum advantage, IBM battery materials, JPMorgan portfolio optimization, and… |
| name | twitter:image | https://quantumcomputingcourses.com/og-default.png |
| name | theme-color | #ffffff |
| name | color-scheme | light dark |
| name | darkreader-lock | |
| name | author | QuantumComputingCourses.com |
| name | google-site-verification | AUHneuZfjppbdqvvRkpwjhiFC2kqmW6SfDDJWqAY_rs |
| property | og:type | website |
| property | og:url | https://quantumcomputingcourses.com/case-studies/ |
| property | og:title | Quantum Computing Case Studies | QuantumComputingCourses.com |
| property | og:description | Real-world quantum computing case studies: Volkswagen traffic routing, Google quantum advantage, IBM battery materials, JPMorgan portfolio optimization, and… |
| property | og:image | https://quantumcomputingcourses.com/og-default.png |
| property | og:image:width | 1200 |
| property | og:image:height | 630 |
| property | og:locale | en_US |
| property | og:site_name | QuantumComputingCourses.com |
Оптимизация Готовность: 80%
Структура
Ошибок нет
Семантические HTML-элементы страницы
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Проверяет наличие основных структурных элементов: nav, header, footer, main. Корректная семантическая структура помогает поисковику понять архитектуру страницы.
Структура документа корректна (теги <html> и <body> присутствуют в единственном экземпляре).
Контент
Ошибок нет
Объём и качество текстового содержимого
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Анализирует объём полезного текста на странице. Слишком мало — страница может считаться малополезной. Слишком много — ухудшается читаемость и восприятие.
Слова из title 5 встречаются в тексте достаточно.
Абзацев с текстом 78 достаточно.
Среднее число слов в абзаце 18 достаточно.
Кол-во знаков контента 24976 на странице оптимально.
Кол-во слов 3344 на странице оптимально.
Заголовки
Ошибок нет
Иерархия заголовков H1–H6
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H1 должен быть один и содержать ключевой запрос. H2–H6 описывают подразделы. Пропуск уровней (H1 → H3) и несколько H1 — типичные ошибки, снижающие понятность страницы для поисковика.
На странице присутствуют заголовки <h1> 1. Это прекрасно.
На странице присутствуют заголовки <h2> 35. Это хорошо.
Тошнота
12,81
Насколько одно слово доминирует в тексте
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Классическая тошнота = √(частота самого повторяющегося слова). Норма до 7–8: текст воспринимается естественно. Выше — поисковик может счесть страницу переспамленной.
Тошнота превышает норму 5. Измените текст страницы!
Академич. тошнота
34,99%
Насколько текст перенасыщен ключевыми словами
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Академическая тошнота = (частота слова / общее количество слов) × 100%. Показывает долю конкретного слова в тексте. Норма 5–15%.
Академическая тошнота превышает норму 5-15%. Измените текст страницы!
Семантическое ядро
20
Наиболее часто встречающиеся слова на странице
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Топ слов по частоте использования. Показывает, какие слова доминируют в тексте с точки зрения поисковика.
Контент страницы содержит осмысленный текст и слова.
Показать список слов
| Слово | Кол-во | Частота |
|---|---|---|
| quantum | 164 | 4,90% |
| outcome | 34 | 1,02% |
| hardware | 24 | 0,72% |
| research | 24 | 0,72% |
| advantage | 22 | 0,66% |
| computing | 21 | 0,63% |
| materials | 20 | 0,60% |
| demonstrated | 19 | 0,57% |
| simulation | 18 | 0,54% |
| classical | 18 | 0,54% |
| optimization | 16 | 0,48% |
| announced | 15 | 0,45% |
| finance | 14 | 0,42% |
| chemistry | 14 | 0,42% |
| production | 13 | 0,39% |
| security | 11 | 0,33% |
| courses | 10 | 0,30% |
| quantinuum | 10 | 0,30% |
| d-wave | 10 | 0,30% |
| algorithm | 10 | 0,30% |
Индексация Готовность: 100%
Индексирование
Ошибок нет
Разрешено ли индексирование страницы
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Проверяет, не закрыта ли страница от индексации через robots.txt, meta robots или X-Robots-Tag. Страница, закрытая от индексации, не появится в поисковой выдаче.
Анкоров на странице 144 оптимально. Поисковые роботы обязательно проиндексируют сайт.
Robots.txt
Найден корректный robots.txt
Файл управления сканированием сайта роботами
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Robots.txt указывает поисковым роботам, какие страницы сканировать, а какие — нет. Ошибки в файле могут случайно закрыть важные разделы от индексации.
Robots.txt настроен корректно. Размер файла: 81 байт. Загружен за: 0сек.
Проверяемая страница не запрещена в robots.txt.
Robots.txt доступен по постоянному адресу
Показать содержимое robots.txt
User-agent: *
Allow: /
Sitemap: https://quantumcomputingcourses.com/sitemap.xml
Sitemap
Кол-во: 1
XML-карта сайта для поисковиков
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Sitemap.xml помогает поисковику быстрее находить и индексировать страницы. Особенно важен для крупных сайтов и новых страниц, на которые ещё нет входящих ссылок.
Robots.txt содержит карту сайта. Это прекрасно!
Robots.txt не содержит ошибок в карте сайта.
Показать карту сайта
| Url | Статус |
|---|---|
| https://quantumcomputingcourses.com/sitemap.xml |
|
Внутренние ссылки
Кол-во: 141
Ссылки на другие страницы своего сайта
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Внутренние ссылки распределяют ссылочный вес между страницами и помогают поисковику обходить сайт. Пустые анкоры и ссылки на запрещённые robots.txt страницы — типичные ошибки.
Внутренних ссылок на странице 141 оптимально.
Внутренние ссылки не запрещены к индексации в robots.txt.
Показать первые 100 внутренних ссылок
| Url | Анкор | Состояние |
|---|---|---|
| / |
<span class="qcc-mark" aria-hidden="true"> <svg xmlns="http://www.w3.org/2000/svg" viewbox="0 0 26 26" fill="none" width="26" height="26"> <path class="qcc-bracket" d="M9 2.6H3.6v20.8H9"></path> <path class="qcc-bracket" d="M17 2.6h5.4v20.8H17"></path> <circle class="qcc-dot" cx="13" cy="13" r="3.6"></circle> </svg> </span> <span class="qcc-word" aria-hidden="true"> <span class="qcc-word-main">QuantumComputing</span> <span class="qcc-word-sub">Courses.com</span> </span>
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| /courses |
Courses <span class="nav-caret" aria-hidden="true"><svg width="9" height="6" viewbox="0 0 10 6" fill="none" aria-hidden="true"><path d="M1 1.3 5 4.9 9 1.3" stroke="currentColor" stroke-width="1.7" stroke-linecap="round" stroke-linejoin="round"></path></svg></span>
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| /courses |
All Courses
|
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| /platform/coursera |
Coursera
|
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| /platform/edx |
edX
|
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| /quantum-computing-udemy |
Udemy
|
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| /platform/brilliant |
Brilliant
|
|
| /platform/google |
Google Quantum AI
|
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| /platform/ibm |
IBM Quantum
|
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| /platform/ionq |
IonQ
|
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| /platform/quantinuum |
Quantinuum
|
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| /platform/aws |
Amazon Braket
|
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| /platform/microsoft |
Azure Quantum
|
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| /platform/quera |
QuEra
|
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| /platform/rigetti |
Rigetti
|
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| /platform/dwave |
D-Wave
|
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| /tutorials |
Tutorials <span class="nav-caret" aria-hidden="true"><svg width="9" height="6" viewbox="0 0 10 6" fill="none" aria-hidden="true"><path d="M1 1.3 5 4.9 9 1.3" stroke="currentColor" stroke-width="1.7" stroke-linecap="round" stroke-linejoin="round"></path></svg></span>
|
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| /tutorials |
All Tutorials
|
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| /tutorials/qiskit-hello-world |
Hello World: Qiskit
|
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| /tutorials/cirq-hello-world |
Hello World: Cirq
|
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| /tutorials/pennylane-hello-world |
Hello World: PennyLane
|
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| /tutorials/braket-hello-world |
Hello World: Braket
|
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| /tutorials/quantum-gates-explained |
Quantum Gates
|
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| /tutorials/grovers-algorithm-explained |
Grover's Algorithm
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| /tutorials/shors-algorithm-explained |
Shor's Algorithm
|
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| /reference |
Reference <span class="nav-caret" aria-hidden="true"><svg width="9" height="6" viewbox="0 0 10 6" fill="none" aria-hidden="true"><path d="M1 1.3 5 4.9 9 1.3" stroke="currentColor" stroke-width="1.7" stroke-linecap="round" stroke-linejoin="round"></path></svg></span>
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| /reference |
All Frameworks
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| /reference/qiskit |
Qiskit
|
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| /reference/cirq |
Cirq
|
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| /reference/pennylane |
PennyLane
|
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| /reference/braket |
Amazon Braket
|
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| /reference/pyquil |
PyQuil
|
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| /reference/tket |
tket
|
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| /reference/ocean |
D-Wave Ocean
|
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| /reference/qsharp |
Q#
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| /case-studies |
Explore <span class="nav-caret" aria-hidden="true"><svg width="9" height="6" viewbox="0 0 10 6" fill="none" aria-hidden="true"><path d="M1 1.3 5 4.9 9 1.3" stroke="currentColor" stroke-width="1.7" stroke-linecap="round" stroke-linejoin="round"></path></svg></span>
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| /learning-paths |
Learning Paths
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| /prerequisites |
Prerequisites
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| /quantum-programming |
Programming Guide
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| /case-studies |
Case Studies
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| /glossary |
Glossary
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| /books |
Books
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| /news |
Quantum News
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| /podcasts |
Podcasts
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| /bloch-sphere |
Bloch Sphere
|
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| /pinball |
Quantum Pinball
|
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| /algorithms |
Algorithm Guide
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| /hardware-guide |
Hardware Guide
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| /qubit-types |
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| /case-studies/bp-quantum-reservoir-simulation |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Energy </span> <time class="study-year" datetime="2026" data-astro-cid-72nlo57a="">2026</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">bp: Quantum Computing for Seismic Imaging and Wave Physics</h2> <p class="study-company" data-astro-cid-72nlo57a="">BP</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">bp and Quantinuum are collaborating on quantum-hybrid algorithms for the wave physics behind subsurface seismic imaging, building on an earlier feasibility pilot. bp has also been an Industry Partner in the IBM Quantum Network since 2021.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Pilot demonstrated feasibility of the approach; the collaboration announced in May 2026 is now scaling to more complex subsurface properties. Research ongoing; no quantum advantage demonstrated yet.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">energy</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">seismic imaging</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">wave physics</span></li> </ul>
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| /case-studies/astrazeneca-quantum-genomics |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Pharma </span> <time class="study-year" datetime="2025" data-astro-cid-72nlo57a="">2025</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">AstraZeneca: Quantum-Accelerated Chemistry Simulation for Drug Synthesis</h2> <p class="study-company" data-astro-cid-72nlo57a="">AstraZeneca</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">AstraZeneca worked with IonQ, AWS, and NVIDIA on a hybrid quantum-classical workflow that simulated a Suzuki-Miyaura reaction, a chemical transformation widely used in small-molecule drug synthesis, combining IonQ's Forte quantum processor with GPU-accelerated classical computation.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">The collaboration demonstrated a large-scale, end-to-end simulation of a Suzuki-Miyaura reaction, described as the most complex chemical simulation run on IonQ hardware to date, and reported more than a 20x improvement in end-to-end time-to-solution versus previous implementations. This is a research demonstration; no production deployment has been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">pharma</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">drug discovery</span></li> </ul>
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| /case-studies/microsoft-quantum-topological-progress |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Security </span> <time class="study-year" datetime="2025" data-astro-cid-72nlo57a="">2025</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Microsoft Topological Qubit Breakthrough: Majorana Zero Modes in 2025</h2> <p class="study-company" data-astro-cid-72nlo57a="">Microsoft</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Microsoft demonstrated its first functional topological qubit based on Majorana zero modes in indium arsenide and aluminum nanowires, using the topological gap protocol (TGP) to confirm non-local qubit encoding that is theoretically protected from local noise without the overhead of conventional error correction.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Demonstrated first functional topological qubit with measurable topological gap; coherence time and gate fidelity characterization ongoing for 2026 scale-up.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">topological qubit</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">Majorana</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">nanowire</span></li> </ul>
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| /case-studies/vodafone-quantum-network-slicing |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Telecom </span> <time class="study-year" datetime="2025" data-astro-cid-72nlo57a="">2025</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Vodafone: Quantum Optimisation for Network Planning with ORCA Computing</h2> <p class="study-company" data-astro-cid-72nlo57a="">Vodafone</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Vodafone partnered with ORCA Computing to run its network optimisation algorithms on a photonic quantum system, targeting fibre cable design and broadband network planning problems such as the Steiner Tree Problem.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Joint testing solved a Steiner Tree Problem for optical fibre design in minutes, a task Vodafone says could take hours to years classically. The collaboration is at the letter-of-intent stage; no production deployment has been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">telecommunications</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">network planning</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">Steiner tree</span></li> </ul>
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| /case-studies/deloitte-quantum-audit-fraud |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Security </span> <time class="study-year" datetime="2024" data-astro-cid-72nlo57a="">2024</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Deloitte Italy: Quantum Machine Learning for Digital Payments Fraud Detection</h2> <p class="study-company" data-astro-cid-72nlo57a="">Deloitte</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Deloitte Italy built a digital payments fraud detection solution using a hybrid quantum neural network on AWS, combining classical Keras layers with a 3-qubit PennyLane quantum layer and deploying the pipeline with Amazon Braket and SageMaker.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">On the public Kaggle credit card fraud dataset, the hybrid quantum model reached 0.92 fraud precision versus 0.86 for a comparable classical baseline, using slightly fewer parameters. Experiments ran on simulators; the design is hardware-ready via Braket.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum ML</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">fraud detection</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">PennyLane</span></li> </ul>
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| /case-studies/mckinsey-quantum-advantage-report |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2024" data-astro-cid-72nlo57a="">2024</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">McKinsey Quantum Technology Monitor: Tracking Industry Quantum Advantage</h2> <p class="study-company" data-astro-cid-72nlo57a="">McKinsey & Company</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">McKinsey's Quantum Technology practice published a landmark study quantifying $1.3 trillion in annual economic value from quantum computing by 2035, released a Quantum Advantage Tracker monitoring 30+ industry projects, and established a time-to-advantage framework distinguishing near-term NISQ optimization wins from fault-tolerant simulation breakthroughs.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Report is widely cited in enterprise quantum strategy and board-level investment cases; McKinsey's Monitor tracks record quantum investment, with cumulative announced public funding alone exceeding $30B alongside record private funding.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum advantage</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">market analysis</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">McKinsey</span></li> </ul>
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| /case-studies/post-quantum-cryptography-migration |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Security </span> <time class="study-year" datetime="2024" data-astro-cid-72nlo57a="">2024</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">NIST and the Post-Quantum Cryptography Standards</h2> <p class="study-company" data-astro-cid-72nlo57a="">NIST / Industry-wide</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">The US National Institute of Standards and Technology ran an 8-year competition to standardize post-quantum cryptography algorithms, producing FIPS 203, 204, and 205 in 2024 - the first global cryptography standards designed to resist quantum attacks.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Three standards published in August 2024: ML-KEM (CRYSTALS-Kyber), ML-DSA (CRYSTALS-Dilithium), and SLH-DSA (SPHINCS+); a fourth, FN-DSA (FALCON, planned as FIPS 206), is still awaiting publication. Governments and enterprises worldwide began migration planning.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">cryptography</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">post-quantum</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">security</span></li> </ul>
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| /case-studies/quantera-eu-quantum-flagship |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Security </span> <time class="study-year" datetime="2024" data-astro-cid-72nlo57a="">2024</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">European Quantum Flagship: 1 Billion Euro Investment in Quantum Technologies</h2> <p class="study-company" data-astro-cid-72nlo57a="">QuantERA / European Quantum Flagship</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">The European Quantum Flagship is a 10-year, 1 billion euro initiative coordinating quantum computing, communication, simulation, and sensing research across dozens of national funding agencies and a broad portfolio of projects, building a sovereign European quantum technology ecosystem.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Flagship funded a broad portfolio of research projects across all four pillars; the European quantum industry has grown rapidly since 2018; IQM brought a 54-qubit superconducting processor to market; the EuroQCI initiative is deploying quantum communication infrastructure across all 27 EU member states.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">security</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum communication</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QKD</span></li> </ul>
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| /case-studies/basf-quantum-chemistry-qft |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Chemicals </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">BASF: Quantum Chemistry Simulation for Catalyst Design</h2> <p class="study-company" data-astro-cid-72nlo57a="">BASF</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">BASF is exploring quantum computing for chemical catalyst development as a founding member of the QUTAC consortium and through a 2023 research partnership with SEEQC focused on homogeneous catalysis.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Early-stage research; BASF estimates roughly 100 high-quality, low-error qubits would be needed even for small-molecule simulation. No quantum advantage demonstrated yet.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">chemistry</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">catalysis</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">molecular simulation</span></li> </ul>
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| /case-studies/cern-quantum-particle-physics |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Machine Learning </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">CERN: Quantum Classifiers for LHC Particle Event Selection</h2> <p class="study-company" data-astro-cid-72nlo57a="">CERN</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">CERN's quantum computing group tested quantum neural networks and parameterized quantum circuits for classifying Large Hadron Collider collision events, comparing quantum classifiers against classical neural networks on Higgs boson signal versus QCD background separation.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Quantum classifiers with 4-8 qubits matched classical networks with similar parameter counts. No quantum advantage observed on tested benchmarks. The barren plateau problem was identified as a key obstacle to scaling. CERN contributed open quantum ML datasets and benchmarks to the research community.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">ml</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">particle physics</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum machine learning</span></li> </ul>
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| /case-studies/citi-quantum-portfolio |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Citigroup: QAOA for Portfolio Optimization with Classiq on AWS Braket</h2> <p class="study-company" data-astro-cid-72nlo57a="">Citigroup</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Citi Innovation Labs worked with Classiq and AWS to explore the Quantum Approximate Optimization Algorithm (QAOA) for portfolio optimization, studying how the algorithm's penalty factor affects performance using Amazon Braket simulators and quantum processing units.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Exploratory study. The team investigated how tuning the QAOA penalty factor influences solution quality and whether QAOA could eventually offer advantages over classical methods for portfolio optimization. No specific performance metrics or production deployment have been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">finance</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">portfolio optimization</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QAOA</span></li> </ul>
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| /case-studies/intel-spin-qubit-manufacturing |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Manufacturing </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Intel Silicon Spin Qubit: Leveraging Semiconductor Fabrication for Quantum Computing</h2> <p class="study-company" data-astro-cid-72nlo57a="">Intel</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Intel's Quantum Research group developed silicon spin qubits fabricated on its 300mm production wafer line, enabling CMOS-compatible qubit manufacturing at semiconductor scale. The Tunnel Falls chip demonstrated 99%+ single-qubit gate fidelity alongside the Horse Ridge II cryogenic control chip.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Achieved 99%+ single-qubit gate fidelity on silicon spin qubits fabricated on 300mm production line; Horse Ridge II enables control of 128 qubits from a single cryogenic chip.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">spin qubits</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">CMOS</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">silicon</span></li> </ul>
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| /case-studies/lockheed-quantum-simulation-materials |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Aerospace </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Lockheed Martin: Quantum Simulation for Aerospace Materials</h2> <p class="study-company" data-astro-cid-72nlo57a="">Lockheed Martin</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Lockheed Martin, one of the earliest commercial quantum computing customers, has pursued quantum simulation of magnetic materials using Heisenberg spin models on trapped ion hardware, with a long-term focus on aerospace materials design.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">D-Wave system acquired in 2011 was used for software verification and optimization tasks. Gate-based research on IonQ trapped ion hardware demonstrated simulation of small Heisenberg spin chain models. Practical quantum advantage for materials simulation is tied to fault-tolerant hardware timelines of a decade or more.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">aerospace</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">materials simulation</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum simulation</span></li> </ul>
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| /case-studies/microsoft-azure-quantum-chemistry |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Energy </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Microsoft Azure Quantum: Simulating Nitrogen Fixation Catalysts</h2> <p class="study-company" data-astro-cid-72nlo57a="">Microsoft Azure Quantum</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Microsoft's Azure Quantum team, in collaboration with academic chemistry partners, applied quantum simulation to the nitrogen fixation problem: understanding how the FeMo-cofactor enzyme catalyzes the conversion of atmospheric nitrogen to ammonia at ambient conditions. The Haber-Bosch industrial process that does this chemically consumes 1-2% of global energy annually; biological nitrogen fixation does the same at room temperature and atmospheric pressure, and understanding its mechanism could enable transformative catalyst design.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Microsoft published resource estimates showing that a fault-tolerant quantum computer with 4,000 logical qubits running for 96 hours could simulate the FeMo-cofactor active site with chemical accuracy, a computation completely intractable classically. Near-term Q# experiments on IonQ and Quantinuum hardware validated the quantum phase estimation subroutines at small scale, establishing confidence in the algorithmic components needed for the full simulation.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">energy</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">nitrogen fixation</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li> </ul>
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| /case-studies/quantinuum-cybersecurity-qkd |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Security </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Quantinuum: Quantum Key Distribution and Certified Randomness for Enterprise Security</h2> <p class="study-company" data-astro-cid-72nlo57a="">Quantinuum</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Quantinuum developed Quantum Origin, a commercial quantum randomness service generating certified entropy from trapped-ion hardware for cryptographic key seeding in enterprise security applications.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Quantum Origin deployed commercially with partners including JPMorgan. Certified quantum randomness integrated into TLS, key management, and certificate pipelines. QKD photon generation demonstrated in research but not yet at commercial scale.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">security</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QKD</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum randomness</span></li> </ul>
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| /case-studies/quantinuum-natural-language-processing |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Machine Learning </span> <time class="study-year" datetime="2023" data-astro-cid-72nlo57a="">2023</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Quantinuum Compositional Quantum Natural Language Processing</h2> <p class="study-company" data-astro-cid-72nlo57a="">Quantinuum</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Quantinuum developed the DisCoCat (Distributional Compositional Categorical) framework for quantum natural language processing, encoding grammatical sentence structure as quantum circuits on their H-series trapped-ion computers. The meaning of a sentence is computed as a tensor contraction of word-state quantum circuits connected by grammatical reduction rules, implemented using the lambeq Python library. Quantinuum ran early binary text classification experiments on small curated sentence datasets using H-series hardware.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Quantinuum reported 87% classification accuracy on the test sentences in an early lambeq experiment on System Model H1 hardware, the first QNLP problem run on that system; demonstrated that grammar-aware quantum circuits preserve sentence structure that bag-of-words models discard.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum-nlp</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">discoco</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">lambeq</span></li> </ul>
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| /case-studies/airbus-fluid-dynamics |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Materials </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Airbus: Quantum Computing for Computational Fluid Dynamics</h2> <p class="study-company" data-astro-cid-72nlo57a="">Airbus</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Airbus explored quantum algorithms for computational fluid dynamics (CFD), specifically targeting the Navier-Stokes equations that model airflow around aircraft wings - one of the most computationally expensive problems in aerospace engineering.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Identified the HHL linear systems algorithm as a candidate for quantum speedup in CFD, demonstrated small-scale implementations, and concluded that fault-tolerant hardware with millions of qubits is required before practical advantage is achievable.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">CFD</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">fluid dynamics</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">HHL algorithm</span></li> </ul>
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| /case-studies/bt-group-quantum-networking |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Telecom </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">BT Group: Quantum Key Distribution Over Live Telecom Fiber</h2> <p class="study-company" data-astro-cid-72nlo57a="">BT Group</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">BT and Toshiba launched the first commercial trial of quantum-secured communication services, a London metro network running QKD over standard Openreach fiber, with EY as the first commercial customer.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Trial network went live in April 2022 connecting EY sites at Canary Wharf and near London Bridge over quantum-secured links. The trial demonstrated commercial feasibility of QKD on standard metro fiber; no nationwide production deployment has been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">telecommunications</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QKD</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum networking</span></li> </ul>
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| /case-studies/mastercard-quantum-fraud-detection |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Mastercard and D-Wave: Quantum Computing for Financial Services</h2> <p class="study-company" data-astro-cid-72nlo57a="">Mastercard</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Mastercard formed a multi-year strategic alliance with D-Wave to explore quantum and quantum-hybrid applications in financial services, including consumer loyalty and rewards, cross-border settlement, fraud management, and anti-money laundering.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Multi-year partnership announced in July 2022. Mastercard gains access to D-Wave's annealing quantum computers and hybrid solvers via the Leap cloud service. No quantitative results or production deployments have been published.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">finance</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">fraud detection</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum annealing</span></li> </ul>
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| /case-studies/nasa-quantum-optimization-missions |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Aerospace </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">NASA: Quantum Annealing for Spacecraft Mission Scheduling</h2> <p class="study-company" data-astro-cid-72nlo57a="">NASA / Ames Research Center</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">NASA's Quantum Artificial Intelligence Laboratory (QuAIL) at Ames Research Center applied quantum annealing on D-Wave hardware and QAOA on IBM Quantum to satellite scheduling and mission planning problems, benchmarking against classical constraint programming.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">D-Wave hybrid solver matched classical constraint programming on scheduling instances up to 1000 variables. QAOA at p=2 approached optimal on 20-variable instances. No speed advantage observed over classical solvers for large problems. NASA continues the program as hardware scales.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">aerospace</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">optimization</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">scheduling</span></li> </ul>
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| /case-studies/volkswagen-battery-quantum-materials |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Materials </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Volkswagen and Xanadu: Quantum Algorithms for Battery Materials</h2> <p class="study-company" data-astro-cid-72nlo57a="">Volkswagen</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Volkswagen Group and quantum computing company Xanadu run a multiyear research program to develop quantum algorithms for simulating battery materials, aiming at safer, lighter, and more cost-effective battery cells.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">First-phase results were published in Physical Review A: an estimate of the quantum resources needed to simulate the cathode material dilithium iron silicate on a future fault-tolerant quantum computer. The work is algorithm research; no hardware deployment or material discovery has been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">battery materials</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum algorithms</span></li> </ul>
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| /case-studies/xanadu-gaussian-boson-sampling |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Materials </span> <time class="study-year" datetime="2022" data-astro-cid-72nlo57a="">2022</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Xanadu: Borealis and Photonic Quantum Advantage</h2> <p class="study-company" data-astro-cid-72nlo57a="">Xanadu</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">In 2022, Xanadu demonstrated quantum computational advantage using Borealis, a programmable photonic quantum computer. The task - Gaussian boson sampling - was completed in 36 microseconds vs an estimated 9000 years on classical supercomputers.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">First demonstration of quantum advantage on a fully programmable photonic device. Borealis made available via Xanadu Cloud for researchers. Published in Nature, June 2022.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">photonic</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">GBS</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum advantage</span></li> </ul>
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| /case-studies/bmw-quantum-annealing-production |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Manufacturing </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">BMW Group: Quantum Optimization for Test Vehicle Production</h2> <p class="study-company" data-astro-cid-72nlo57a="">BMW Group</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">BMW Group's Quantum Computing Challenge posed real production planning problems to the quantum community, including optimizing pre-production test vehicle configuration. Researchers tackled it with D-Wave's hybrid constrained quadratic model solver.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Independent researchers solved BMW's test vehicle use case with D-Wave's hybrid CQM solver and found its performance comparable to classical solvers such as CBC and Gurobi. No quantum advantage was demonstrated; the value was an honest, reproducible benchmark on a real industrial problem.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">manufacturing</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">scheduling</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum annealing</span></li> </ul>
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| /case-studies/boehringer-ingelheim-quantum-chemistry |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Pharma </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Boehringer Ingelheim: Quantum Chemistry Partnership with Google Quantum AI</h2> <p class="study-company" data-astro-cid-72nlo57a="">Boehringer Ingelheim / Google Quantum AI</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">In January 2021 Boehringer Ingelheim announced a three-year partnership with Google Quantum AI to research quantum computing use cases in pharmaceutical R&D, focusing on molecular dynamics simulations and quantum chemistry. Boehringer was the first pharmaceutical company to partner with Google in quantum computing.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Exploratory three-year research partnership announced January 2021, co-led by Boehringer Ingelheim's newly established Quantum Lab. It pairs Boehringer's computer-aided drug design expertise with Google's quantum hardware and algorithms. No specific simulation results, hardware benchmarks, or production deployment have been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">pharma</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">VQE</span></li> </ul>
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| /case-studies/exxonmobil-energy-optimization |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Energy </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">ExxonMobil: Quantum Optimisation for Shipping Route Planning</h2> <p class="study-company" data-astro-cid-72nlo57a="">ExxonMobil / IBM Quantum</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">ExxonMobil partnered with IBM Quantum to apply QAOA and quantum optimisation to maritime shipping route planning, targeting fuel consumption reduction across their global LNG tanker fleet.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Demonstrated that quantum-classical hybrid QAOA approaches can find solutions competitive with classical solvers for small instances, with a roadmap to practical advantage as error rates improve.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QAOA</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">optimisation</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">shipping</span></li> </ul>
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| /case-studies/goldman-sachs-quantum-monte-carlo |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Goldman Sachs: Quantum Amplitude Estimation for Option Pricing</h2> <p class="study-company" data-astro-cid-72nlo57a="">Goldman Sachs</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Goldman Sachs applied Quantum Amplitude Estimation to European and Asian option pricing, publishing detailed resource analyses showing the qubit counts and error rates required for practical quantum advantage over classical Monte Carlo.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Confirmed QAE provides quadratic speedup in query complexity over classical Monte Carlo. Resource analysis with IBM found practical advantage would require roughly 8,000 logical qubits running at about a 10 MHz logical clock rate, well beyond current hardware. Identified near-term hybrid approaches as interim strategy.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">finance</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">Monte Carlo</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">amplitude estimation</span></li> </ul>
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| /case-studies/jpmorgan-portfolio-optimization |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">JPMorgan: Quantum Portfolio Optimization with QAOA</h2> <p class="study-company" data-astro-cid-72nlo57a="">JPMorgan Chase</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">JPMorgan's quantum computing research team applied the Quantum Approximate Optimization Algorithm (QAOA) to portfolio selection problems, benchmarking it against classical methods on current NISQ hardware.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">QAOA matched classical solution quality on small instances (up to 60 assets). Identified that quantum advantage in finance requires error-corrected hardware. Published multiple peer-reviewed papers establishing methodology for finance use cases.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">finance</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">portfolio optimization</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QAOA</span></li> </ul>
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| /case-studies/toshiba-quantum-key-distribution |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Telecom </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Toshiba Research: Quantum Key Distribution Over 600km Fiber</h2> <p class="study-company" data-astro-cid-72nlo57a="">Toshiba Research</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Toshiba's UK research lab demonstrated twin-field QKD over 600km of standard fiber optic cable, setting a distance record and achieving key rates sufficient for AES-256 key refresh in real financial networks.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Achieved 600km QKD over standard optical fiber, far beyond the 100-200km range of commercial QKD systems at the time. Key rates were sufficient for real network use with AES-256 key refresh. Toshiba is commercializing QKD systems and has worked with partners including BT on quantum-secure network trials.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum communication</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QKD</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">BB84</span></li> </ul>
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| /case-studies/toyota-quantum-battery-optimization |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Materials </span> <time class="study-year" datetime="2021" data-astro-cid-72nlo57a="">2021</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Toyota and QunaSys: Quantum Computing for EV Battery Materials</h2> <p class="study-company" data-astro-cid-72nlo57a="">Toyota Motor</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Toyota partnered with Japanese quantum software startup QunaSys to research new EV battery materials, exploring how quantum computers can improve the accuracy and speed of electronic structure simulation.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Joint research announced in October 2021, with the partners considering use of Japan's first commercial IBM quantum computer in Kawasaki. The work is exploratory materials research; no material discoveries or production results have been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">materials</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">battery</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li> </ul>
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| /case-studies/airbus-satellite-optimization |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Aerospace </span> <time class="study-year" datetime="2020" data-astro-cid-72nlo57a="">2020</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Airbus: Quantum Optimization for Satellite Constellation Scheduling</h2> <p class="study-company" data-astro-cid-72nlo57a="">Airbus</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Airbus Defence and Space formulated satellite imaging scheduling as a QUBO problem and benchmarked D-Wave quantum annealing and QAOA against classical constraint programming for constellation management.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Quantum annealing solved Earth-observation scheduling instances faster than exact solvers in some cases, matching heuristic solution quality on small instances. Full constellation management still benefits from quantum only as a subproblem solver.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">aerospace</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">satellite</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">scheduling</span></li> </ul>
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| /case-studies/ibm-drug-discovery |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Materials </span> <time class="study-year" datetime="2020" data-astro-cid-72nlo57a="">2020</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">IBM and Daimler: Quantum Chemistry for Battery Materials</h2> <p class="study-company" data-astro-cid-72nlo57a="">IBM Quantum / Daimler</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">IBM and Daimler AG (now Mercedes-Benz) collaborated to simulate the lithium hydride molecule using VQE on IBM quantum hardware, exploring quantum chemistry for next-generation battery design.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Successfully simulated LiH molecular energy using VQE on real hardware. Demonstrated that noisy quantum hardware + error mitigation can produce chemically meaningful results at small scale.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">quantum chemistry</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">VQE</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">battery</span></li> </ul>
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| /case-studies/barclays-quantum-risk-optimization |
<span class="study-meta" data-astro-cid-72nlo57a=""> <span class="sector-badge tag" data-astro-cid-72nlo57a=""> Finance </span> <time class="study-year" datetime="2019" data-astro-cid-72nlo57a="">2019</time> </span> <h2 class="study-title" data-astro-cid-72nlo57a="">Barclays: Quantum Algorithms for Securities Settlement Optimization</h2> <p class="study-company" data-astro-cid-72nlo57a="">Barclays</p> <p class="study-desc muted" data-astro-cid-72nlo57a="">Barclays' chief technology and innovation office worked with IBM to develop and test a proof-of-concept quantum algorithm for optimizing securities transaction settlement, running core parts of the problem on a seven-qubit IBM cloud quantum computer.</p> <dl class="study-outcome" data-astro-cid-72nlo57a=""> <dt class="outcome-label" data-astro-cid-72nlo57a="">Outcome</dt> <dd class="outcome-text" data-astro-cid-72nlo57a="">Proof of concept demonstrated on a seven-qubit IBM device. The team showed that settlement features of sufficient complexity could be expressed and explored with quantum methods, but stated that any practical advantage would only appear at large scale (tens of thousands of trades). No production deployment has been announced.</dd> </dl> <ul class="taglist" data-astro-cid-72nlo57a=""> <li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">finance</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">settlement</span></li><li data-astro-cid-72nlo57a=""><span class="tag" data-astro-cid-72nlo57a="">QAOA</span></li> </ul>
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