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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, viewport-fit=cover">
<title>qFold on Amazon Braket - Multi-language Guide</title>
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</head>
<body>
<div class="top-bar">
<div class="partner-logos">
<img src="https://qfoldit.github.io/img/1/logo/iDMT_logo.png" alt="iDMT Logo" title="iDMT - Innovation Centre in Digital Molecular Technologies">
<img src="https://qfoldit.github.io/img/h3d_final_logo.png" alt="H3D Logo" title="H3D - Holistic Drug Discovery and Development Centre">
</div>
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</div>
<div class="card">
<div class="header-container">
<div class="logo-area">
<img src="https://qfoldit.github.io/img/1/logo/amazon-braket.svg" alt="Amazon Braket Logo" class="logo">
<h1 data-i18n="title">qFold on <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></h1>
</div>
</div>
<p class="description" data-i18n="description">Quick guide to deploying the quantum protein folding algorithm on <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a>.</p>
<!-- Step 1 -->
<div class="step">
<div class="step-number">1</div>
<div class="step-text" data-i18n="step1">
<strong>Console access:</strong> Log in to your AWS account and navigate to <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong>. Make sure you are in a region that supports the required devices (e.g., <em>us-east-1</em>).
</div>
</div>
<!-- Step 2 -->
<div class="step">
<div class="step-number">2</div>
<div class="step-text" data-i18n="step2">
<strong>Create a notebook:</strong> In the left menu, select <strong>Notebooks</strong> → <strong>Create notebook instance</strong>.
<ul>
<li>Name: <code>qfold-instance</code></li>
<li>Type: <code>ml.t3.medium</code> (minimum sufficient)</li>
</ul>
</div>
</div>
<!-- Step 3 -->
<div class="step">
<div class="step-number">3</div>
<div class="step-text" data-i18n="step3">
<strong>Open Jupyter:</strong> When the status changes to <span style="color: green;">InService</span>, click <strong>Open JupyterLab</strong>.
</div>
</div>
<!-- Step 4 -->
<div class="step">
<div class="step-number">4</div>
<div class="step-text" data-i18n="step4">
<strong>Find qFold:</strong> Navigate to <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> and open the file <code><a href="https://github.com/qfoldit/AWS-Deploy/tree/main/source/src/notebook/healthcare-and-life-sciences/c-1-protein-folding-quantum-random-walk" target="blank">qfold.ipynb</a></code>.
</div>
</div>
<!-- Step 5 -->
<div class="step">
<div class="step-number">5</div>
<div class="step-text" data-i18n="step5">
<strong>Device configuration:</strong> In the code, find the <code>device</code> variable.
<ul>
<li>For free test: <code>LocalSimulator()</code></li>
<li>For QPU: provide ARN (e.g., Rigetti or IonQ).</li>
</ul>
</div>
</div>
<!-- Warning -->
<div class="warning" data-i18n="warning">
<strong>Note:</strong> Running on real QPU is paid. First debug on a local simulator.
</div>
<!-- Step 6 -->
<div class="step">
<div class="step-number">6</div>
<div class="step-text" data-i18n="step6">
<strong>Run:</strong> Click <em>Run → Run All Cells</em>. The protein structure optimization results will appear in the bottom cells.
</div>
</div>
<!-- Встроенная консоль -->
<div class="console-section">
<div class="console-title">
<i class="fas fa-terminal"></i> <span data-i18n="console_title">Interactive Console </span>
</div>
<div class="console-window">
<div class="console-output" id="consoleOutput">
<div class="console-line"><span class="console-prompt">$</span> <span>Welcome to qFold demo terminal</span></div>
<div class="console-line"><span class="console-prompt">$</span> <span>Type 'help' to see available commands</span></div>
<div class="console-line"><span class="console-prompt">$</span> <span>Example: device = LocalSimulator()</span></div>
<div class="console-line"><span class="console-prompt">$</span> <span>─────────────────────────────</span></div>
</div>
<div class="console-input-line">
<span class="console-input-prompt">❯</span>
<input type="text" class="console-input" id="consoleInput" placeholder="Enter command... (help for list)" autocomplete="off">
</div>
</div>
<div class="console-help">
💡 <span>help</span> — show commands | <span>clear</span> — clear console | <span>run_qfold</span> — simulate qFold execution
</div>
</div>
</div>
<div class="footer" data-i18n="footer">
Quick start with qFold in the <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS ecosystem</a>.<br>
<i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>
</div>
<script>
// ---------- TRANSLATIONS ----------
const translations = {
en: {
title: 'qFold on <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a>',
description: 'Quick guide to deploying the quantum protein folding algorithm on <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a>.',
step1: '<strong>Console access:</strong> Log in to your AWS account and navigate to <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong>. Make sure you are in a region that supports the required devices (e.g., <em>us-east-1</em>).',
step2: '<strong>Create a notebook:</strong> In the left menu, select <strong>Notebooks</strong> → <strong>Create notebook instance</strong>.<br><ul><li>Name: <code>qfold-instance</code></li><li>Type: <code>ml.t3.medium</code> (minimum sufficient)</li></ul>',
step3: '<strong>Open Jupyter:</strong> When the status changes to <span style="color: green;">InService</span>, click <strong>Open JupyterLab</strong>.',
step4: '<strong>Find qFold:</strong> Navigate to <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> and open the file <code>qfold.ipynb</code>.',
step5: '<strong>Device configuration:</strong> In the code, find the <code>device</code> variable.<br><ul><li>For free test: <code>LocalSimulator()</code></li><li>For QPU: provide ARN (e.g., Rigetti or IonQ).</li></ul>',
warning: '<strong>Note:</strong> Running on real QPU is paid. First debug on a local simulator.',
step6: '<strong>Run:</strong> Click <em>Run → Run All Cells</em>. The protein structure optimization results will appear in the bottom cells.',
footer: 'Quick start with qFold in the <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS ecosystem</a>.<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: 'Interactive Console '
},
de: {
title: 'qFold auf <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a>',
description: 'Kurzanleitung zur Bereitstellung des Quanten-Faltungsalgorithmus für Proteine auf <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a>.',
step1: '<strong>Konsolenzugriff:</strong> Melden Sie sich bei Ihrem AWS-Konto an und navigieren Sie zu <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong>. Stellen Sie sicher, dass Sie sich in einer Region befinden, die die erforderlichen Geräte unterstützt (z. B. <em>us-east-1</em>).',
step2: '<strong>Notebook erstellen:</strong> Wählen Sie im linken Menü <strong>Notebooks</strong> → <strong>Create notebook instance</strong>.<br><ul><li>Name: <code>qfold-instance</code></li><li>Typ: <code>ml.t3.medium</code> (minimal ausreichend)</li></ul>',
step3: '<strong>Jupyter öffnen:</strong> Wenn der Status auf <span style="color: green;">InService</span> wechselt, klicken Sie auf <strong>Open JupyterLab</strong>.',
step4: '<strong>qFold finden:</strong> Navigieren Sie zu <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> und öffnen Sie die Datei <code>qfold.ipynb</code>.',
step5: '<strong>Gerätekonfiguration:</strong> Suchen Sie im Code nach der Variablen <code>device</code>.<br><ul><li>Für kostenlosen Test: <code>LocalSimulator()</code></li><li>Für QPU: ARN angeben (z. B. Rigetti oder IonQ).</li></ul>',
warning: '<strong>Hinweis:</strong> Die Ausführung auf echten QPUs ist kostenpflichtig. Debuggen Sie zuerst auf einem lokalen Simulator.',
step6: '<strong>Ausführen:</strong> Klicken Sie auf <em>Run → Run All Cells</em>. Die Ergebnisse der Proteinoptimierung erscheinen in den unteren Zellen.',
footer: 'Schnelleinstieg mit qFold im <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS-Ökosystem</a>.<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: 'Interaktive Konsole '
},
fr: {
title: 'qFold sur <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a>',
description: 'Guide rapide pour déployer l\'algorithme quantique de repliement des protéines sur <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a>.',
step1: '<strong>Accès à la console :</strong> Connectez-vous à votre compte AWS et accédez à <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong>. Assurez-vous d\'être dans une région prenant en charge les appareils requis (par exemple <em>us-east-1</em>).',
step2: '<strong>Créer un notebook :</strong> Dans le menu de gauche, sélectionnez <strong>Notebooks</strong> → <strong>Create notebook instance</strong>.<br><ul><li>Nom : <code>qfold-instance</code></li><li>Type : <code>ml.t3.medium</code> (minimum suffisant)</li></ul>',
step3: '<strong>Ouvrir Jupyter :</strong> Lorsque le statut passe à <span style="color: green;">InService</span>, cliquez sur <strong>Open JupyterLab</strong>.',
step4: '<strong>Trouver qFold :</strong> Accédez à <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> et ouvrez le fichier <code>qfold.ipynb</code>.',
step5: '<strong>Configuration de l\'appareil :</strong> Dans le code, trouvez la variable <code>device</code>.<br><ul><li>Pour un test gratuit : <code>LocalSimulator()</code></li><li>Pour QPU : fournissez l\'ARN (ex. Rigetti ou IonQ).</li></ul>',
warning: '<strong>Remarque :</strong> L\'exécution sur un vrai QPU est payante. Déboguez d\'abord sur un simulateur local.',
step6: '<strong>Exécuter :</strong> Cliquez sur <em>Run → Run All Cells</em>. Les résultats d\'optimisation de la structure des protéines apparaîtront dans les cellules du bas.',
footer: 'Démarrage rapide avec qFold dans l\'<a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">écosystème AWS</a>.<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: 'Console interactive '
},
ru: {
title: 'qFold на <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a>',
description: 'Краткое руководство по развертыванию квантового алгоритма сворачивания белка на <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a>.',
step1: '<strong>Доступ к консоли:</strong> Войдите в свою учетную запись AWS и перейдите в сервис <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong>. Убедитесь, что вы находитесь в регионе, поддерживающем нужные устройства (например, <em>us-east-1</em>).',
step2: '<strong>Создание блокнота:</strong> В левом меню выберите <strong>Notebooks</strong> → <strong>Create notebook instance</strong>.<br><ul><li>Имя: <code>qfold-instance</code></li><li>Тип: <code>ml.t3.medium</code> (минимально достаточный)</li></ul>',
step3: '<strong>Открытие Jupyter:</strong> Когда статус сменится на <span style="color: green;">InService</span>, нажмите <strong>Open JupyterLab</strong>.',
step4: '<strong>Поиск qFold:</strong> Перейдите по пути: <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> и откройте файл <code>qfold.ipynb</code>.',
step5: '<strong>Настройка устройства:</strong> В коде найдите переменную <code>device</code>.<br><ul><li>Для бесплатного теста: <code>LocalSimulator()</code></li><li>Для QPU: укажите ARN (например, Rigetti или IonQ).</li></ul>',
warning: '<strong>Внимание:</strong> Запуск на реальных квантовых процессорах (QPU) платный. Сначала отладьте код на локальном симуляторе.',
step6: '<strong>Запуск:</strong> Нажмите <em>Run → Run All Cells</em>. Результаты оптимизации структуры белка появятся в нижних ячейках.',
footer: 'Быстрый старт с qFold в <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">экосистеме AWS</a>.<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: 'Интерактивная консоль '
},
ja: {
title: '<a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a> での qFold',
description: '<a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a> での量子タンパク質フォールディングアルゴリズム展開のクイックガイド。',
step1: '<strong>コンソールアクセス:</strong> AWS アカウントにログインし、<strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong> サービスに移動します。必要なデバイスをサポートするリージョン（例: <em>us-east-1</em>）にいることを確認してください。',
step2: '<strong>ノートブックの作成:</strong> 左側のメニューから <strong>Notebooks</strong> → <strong>Create notebook instance</strong> を選択します。<br><ul><li>名前: <code>qfold-instance</code></li><li>タイプ: <code>ml.t3.medium</code> (最小限で十分)</li></ul>',
step3: '<strong>Jupyter を開く:</strong> ステータスが <span style="color: green;">InService</span> に変わったら、<strong>Open JupyterLab</strong> をクリックします。',
step4: '<strong>qFold を見つける:</strong> <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> に移動し、<code>qfold.ipynb</code> ファイルを開きます。',
step5: '<strong>デバイス設定:</strong> コード内で <code>device</code> 変数を見つけます。<br><ul><li>無料テストの場合: <code>LocalSimulator()</code></li><li>QPU の場合: ARN を指定します（例: Rigetti または IonQ）。</li></ul>',
warning: '<strong>注意:</strong> 実際の QPU での実行は有料です。まずローカルシミュレーターでデバッグしてください。',
step6: '<strong>実行:</strong> <em>Run → Run All Cells</em> をクリックします。タンパク質構造の最適化結果が下部のセルに表示されます。',
footer: 'AWS エコシステムで qFold を迅速に開始するために作成されました。<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: 'インタラクティブコンソール '
},
zh: {
title: '<a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a> 上的 qFold',
description: '在 <a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">AWS Braket</a> 上部署量子蛋白质折叠算法的快速指南。',
step1: '<strong>控制台访问：</strong> 登录您的 AWS 账户，进入 <strong><a href="https://aws.amazon.com/braket/" target="_blank" rel="noopener">Amazon Braket</a></strong> 服务。确保您所在的区域支持所需的设备（例如 <em>us-east-1</em>）。',
step2: '<strong>创建笔记本：</strong> 在左侧菜单中选择 <strong>Notebooks</strong> → <strong>Create notebook instance</strong>。<br><ul><li>名称：<code>qfold-instance</code></li><li>类型：<code>ml.t3.medium</code>（最低配置足够）</li></ul>',
step3: '<strong>打开 Jupyter：</strong> 当状态变为 <span style="color: green;">InService</span> 时，点击 <strong>Open JupyterLab</strong>。',
step4: '<strong>查找 qFold：</strong> 导航到 <code>amazon-braket-examples / examples / hybrid_quantum_algorithms / qfold</code> 并打开文件 <code>qfold.ipynb</code>。',
step5: '<strong>设备配置：</strong> 在代码中找到变量 <code>device</code>。<br><ul><li>免费测试：<code>LocalSimulator()</code></li><li>使用 QPU：提供 ARN（例如 Rigetti 或 IonQ）。</li></ul>',
warning: '<strong>注意：</strong> 在真实 QPU 上运行需要付费。请先在本地模拟器上进行调试。',
step6: '<strong>运行：</strong> 点击 <em>Run → Run All Cells</em>。蛋白质结构优化结果将显示在底部单元格中。',
footer: '为在 AWS 生态系统中快速启动 qFold 而创建。<br><i class="fab fa-github github-icon"></i> <a href="https://github.com/qfoldit" target="_blank" rel="noopener">github.com/qfoldit</a>',
console_title: '交互式控制台（演示）'
}
};
// DOM elements for i18n
const elements = {
title: document.querySelector('[data-i18n="title"]'),
description: document.querySelector('[data-i18n="description"]'),
step1: document.querySelector('[data-i18n="step1"]'),
step2: document.querySelector('[data-i18n="step2"]'),
step3: document.querySelector('[data-i18n="step3"]'),
step4: document.querySelector('[data-i18n="step4"]'),
step5: document.querySelector('[data-i18n="step5"]'),
warning: document.querySelector('[data-i18n="warning"]'),
step6: document.querySelector('[data-i18n="step6"]'),
footer: document.querySelector('[data-i18n="footer"]'),
console_title: document.querySelector('[data-i18n="console_title"]')
};
let currentLang = localStorage.getItem('qfold_lang') || 'en';
function setLanguage(lang) {
if (!translations[lang]) return;
for (const key in elements) {
if (elements[key] && translations[lang][key]) {
elements[key].innerHTML = translations[lang][key];
}
}
// Обновляем активный класс у кнопок
document.querySelectorAll('.lang-btn').forEach(btn => {
const btnLang = btn.getAttribute('data-lang');
if (btnLang === lang) {
btn.classList.add('active');
} else {
btn.classList.remove('active');
}
});
localStorage.setItem('qfold_lang', lang);
document.documentElement.lang = lang;
}
document.querySelectorAll('.lang-btn').forEach(btn => {
btn.addEventListener('click', () => setLanguage(btn.getAttribute('data-lang')));
});
setLanguage(currentLang);
// ---------- КОНСОЛЬ (демонстрационная) ----------
const outputDiv = document.getElementById('consoleOutput');
const inputField = document.getElementById('consoleInput');
function addLine(text, isCommand = false) {
const lineDiv = document.createElement('div');
lineDiv.className = 'console-line';
if (isCommand) {
lineDiv.innerHTML = `<span class="console-prompt">❯</span> <span>${escapeHtml(text)}</span>`;
} else {
lineDiv.innerHTML = `<span class="console-prompt">$</span> <span>${escapeHtml(text)}</span>`;
}
outputDiv.appendChild(lineDiv);
outputDiv.scrollTop = outputDiv.scrollHeight;
}
function escapeHtml(str) {
return str.replace(/[&<>]/g, function(m) {
if (m === '&') return '&';
if (m === '<') return '<';
if (m === '>') return '>';
return m;
}).replace(/[\uD800-\uDBFF][\uDC00-\uDFFF]/g, function(c) {
return c;
});
}
function clearConsole() {
outputDiv.innerHTML = '';
addLine('Console cleared. Type help for commands.');
}
function processCommand(cmd) {
const trimmed = cmd.trim().toLowerCase();
if (trimmed === 'clear') {
clearConsole();
return;
}
if (trimmed === 'help') {
addLine('Available commands:');
addLine(' help – show this help');
addLine(' clear – clear console output');
addLine(' run_qfold – simulate qFold execution (local simulator)');
addLine(' device – show current device info');
addLine(' LocalSimulator() – display simulator status');
addLine(' <any python-like> – demo response');
return;
}
if (trimmed === 'run_qfold') {
addLine('Running qFold with LocalSimulator()...');
setTimeout(() => {
addLine('✅ Quantum circuit executed.');
addLine('📊 Optimized protein structure: energy = -12.37 kcal/mol');
addLine('💡 Folding pathway predicted. See notebook for full details.');
}, 300);
return;
}
if (trimmed === 'device') {
addLine('Current device: LocalSimulator() (free, runs on CPU)');
addLine('To use QPU, set device = AwsDevice("arn:aws:braket:...")');
return;
}
if (trimmed === 'localsimulator()') {
addLine('LocalSimulator() initialized. Backend: "braket_sv" (state vector)');
addLine('You can now run circuits: task = device.run(circuit, shots=1000)');
return;
}
// Fallback для любых других команд (демонстрация)
if (cmd.trim().length > 0) {
addLine(`Command '${cmd}' not recognized. Type 'help' for available commands.`);
}
}
inputField.addEventListener('keypress', (e) => {
if (e.key === 'Enter') {
const cmd = inputField.value;
if (cmd.trim().length > 0) {
addLine(cmd, true);
processCommand(cmd);
}
inputField.value = '';
}
});
</script>
</body>
</html>