From 6299427cc64bb1af979eaf668b011ae7774dab79 Mon Sep 17 00:00:00 2001
From: "Dynex [DNX]" <113523376+dynexcoin@users.noreply.github.com>
Date: Tue, 13 Aug 2024 09:18:24 +0200
Subject: [PATCH] Add files via upload

---
 circuit_example_quantum_transformer.ipynb | 264 ++++++++++++++++++++++
 1 file changed, 264 insertions(+)
 create mode 100644 circuit_example_quantum_transformer.ipynb

diff --git a/circuit_example_quantum_transformer.ipynb b/circuit_example_quantum_transformer.ipynb
new file mode 100644
index 0000000..a14448c
--- /dev/null
+++ b/circuit_example_quantum_transformer.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1fa94d56-57dd-4cc6-beda-9100e769ee4f",
+   "metadata": {},
+   "source": [
+    "# Dynex SDK - Quantum Transformer Native Gate Circuit Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ac20d6e-4232-437f-9b24-cb5d57759d38",
+   "metadata": {},
+   "source": [
+    "### Quantum Self-Attention Transformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb9b3a76-63f2-4256-ac5c-67b94d3105a3",
+   "metadata": {},
+   "source": [
+    "The Quantum Self-Attention Transformer leverages the principles of quantum computing to perform tasks typically handled by classical transformers, particularly in the domain of NLP and LLM. Classical transformers rely heavily on the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence when making predictions or generating new text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d63e025d-8aeb-4840-ac69-cd6430043608",
+   "metadata": {},
+   "source": [
+    "First we import the required packages:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3258cd0a-8fb4-4ce7-89a1-4120322ef52c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
+      "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dynex\n",
+    "from dynex import dynex_circuit\n",
+    "from pennylane import numpy as np\n",
+    "import pennylane as qml\n",
+    "\n",
+    "from gensim.models import Word2Vec\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import nltk            #run it only for the first time please\n",
+    "nltk.download('punkt') #run it only for the first time please\n",
+    "nltk.download('punkt_tab') #run it only for the first time please"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4b4dad6-3b7d-4b17-93d5-4ab82c8b5aef",
+   "metadata": {},
+   "source": [
+    "We define our circuit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f1e789a4-612d-433f-95fb-43cebab281f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sentences = [\n",
+    "    \"Dynex powers Quantum entanglement\",\n",
+    "    \"Neuromorphic networks process qubits\",\n",
+    "    \"Dynex solves optimization problems\",\n",
+    "    \"Entangled qubits process information\"\n",
+    "]\n",
+    "\n",
+    "tokenization = [word_tokenize(sentence.lower()) for sentence in sentences]\n",
+    "word2vec = Word2Vec(sentences=tokenization, vector_size=8, window=5, min_count=1, workers=4)\n",
+    "qubits = 8\n",
+    "dev = qml.device(\"default.qubit\", wires=qubits)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def QuantumSelfAttention(inputs):\n",
+    "    biIn = [1 if x >= 0 else 0 for x in inputs]\n",
+    "    qml.BasisEmbedding(biIn, wires=range(qubits))\n",
+    "    for layer in range(3):\n",
+    "        for i in range(qubits):\n",
+    "            qml.RX(inputs[i % len(inputs)] * (layer + 1), wires=i)\n",
+    "            qml.RY(inputs[(i + 1) % len(inputs)] * (layer + 1), wires=i)\n",
+    "            qml.RZ(inputs[(i + 2) % len(inputs)] * (layer + 1), wires=i)\n",
+    "        for i in range(qubits - 1):\n",
+    "            qml.CRZ(np.pi / (layer + 2), wires=[i, (i + 1) % qubits])\n",
+    "            qml.CNOT(wires=[i, (i + 1) % qubits])\n",
+    "        for i in range(qubits - 2):\n",
+    "            qml.CRX(inputs[i % len(inputs)], wires=[i, (i + 2) % qubits])\n",
+    "            qml.CRY(inputs[(i + 1) % len(inputs)], wires=[i, (i + 2) % qubits])\n",
+    "            \n",
+    "    qml.QFT(wires=range(qubits))\n",
+    "    qml.adjoint(qml.QFT)(wires=range(qubits))\n",
+    "    qml.GroverOperator(wires=range(qubits))\n",
+    "    for i in range(qubits):\n",
+    "        qml.Hadamard(wires=i)\n",
+    "        qml.T(wires=i)\n",
+    "        qml.RZ(inputs[i % len(inputs)], wires=i)\n",
+    "    qml.BasisEmbedding(biIn, wires=range(qubits))\n",
+    "    return [qml.expval(qml.PauliZ(wires=i)) for i in range(qubits)]\n",
+    "\n",
+    "dim = [-0.00532336, 0.00226685, 0.05092087, 0.0901195, -0.09305907, -0.07108232, 0.06466064, 0.0802789]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e37c1084-a8ad-48fe-8ad3-78dc49b3a55d",
+   "metadata": {},
+   "source": [
+    "We draw the circuit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5a8a1f3f-8984-49b6-a644-a0e403e25811",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 5800x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw circuit:\n",
+    "_ = qml.draw_mpl(QuantumSelfAttention, style=\"black_white\")(dim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9f9d7e-213e-40e3-9362-fd57faebab66",
+   "metadata": {},
+   "source": [
+    "We execute and measure the circuit on the Dynex platform:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "248c2c08-935c-49a1-a81f-ca03d23e3cc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "╭────────────┬──────────┬─────────────────┬─────────────┬───────────┬────────────────┬────────────┬─────────┬────────────────╮\n",
+      "│   DYNEXJOB │   QUBITS │   QUANTUM GATES │   BLOCK FEE │   ELAPSED │   WORKERS READ │   CIRCUITS │   STEPS │   GROUND STATE │\n",
+      "├────────────┼──────────┼─────────────────┼─────────────┼───────────┼────────────────┼────────────┼─────────┼────────────────┤\n",
+      "│      30238 │      100 │             554 │        0.19 │      0.41 │              1 │       1000 │     100 │      547936.00 │\n",
+      "╰────────────┴──────────┴─────────────────┴─────────────┴───────────┴────────────────┴────────────┴─────────┴────────────────╯\n",
+      "╭────────────┬─────────────────┬────────────┬───────┬──────────┬─────────────┬─────────────────────────────┬───────────┬──────────╮\n",
+      "│     WORKER │         VERSION │   CIRCUITS │   LOC │   ENERGY │     RUNTIME │                 LAST UPDATE │     STEPS │   STATUS │\n",
+      "├────────────┼─────────────────┼────────────┼───────┼──────────┼─────────────┼─────────────────────────────┼───────────┼──────────┤\n",
+      "│ 1147..9be1 │ 2.3.5.OZM.134.L │       1000 │     0 │     0.00 │ 3.35030083s │ 2024-08-13T07:15:16.148252Z │ 0 (0.00%) │  \u001b[1;31mSTOPPED\u001b[0m │\n",
+      "├────────────┼─────────────────┼────────────┼───────┼──────────┼─────────────┼─────────────────────────────┼───────────┼──────────┤\n",
+      "│ 150d..101f │ 2.3.5.OZM.134.L │       1000 │     0 │     0.00 │ 8.23780587s │ 2024-08-13T07:15:11.260748Z │ 0 (0.00%) │  \u001b[1;31mSTOPPED\u001b[0m │\n",
+      "╰────────────┴─────────────────┴────────────┴───────┴──────────┴─────────────┴─────────────────────────────┴───────────┴──────────╯\n",
+      "[DYNEX] FINISHED READ AFTER 40.92 SECONDS\n",
+      "[DYNEX] SAMPLESET READY\n",
+      "Generated: quantum powers networks optimization\n"
+     ]
+    }
+   ],
+   "source": [
+    "def softmax(x):\n",
+    "    return np.exp(x) / np.sum(np.exp(x), axis=0)\n",
+    "\n",
+    "def GenerateSentence(input_):\n",
+    "    tokens = word_tokenize(input_.lower())\n",
+    "    iEmbeddings = np.array([word2vec.wv[word] for word in tokens])\n",
+    "    attOutputs = []\n",
+    "    for embedding in iEmbeddings:\n",
+    "        attOUT = dynex_circuit.execute(QuantumSelfAttention, embedding, qubits, method='measure', num_reads=1000, \n",
+    "                                 integration_steps=100, debugging=False, mainnet=True, is_cluster=True, shots=1)\n",
+    "        softOUT = softmax(attOUT)\n",
+    "        attOutputs.append(softOUT)\n",
+    "    generated = []\n",
+    "    while len(generated) < 4:\n",
+    "        for i, output in enumerate(attOutputs):\n",
+    "            if len(generated) >= 4:\n",
+    "                break\n",
+    "            combVec = 0.5 * iEmbeddings[i] + 0.5 * output\n",
+    "            clWords = word2vec.wv.similar_by_vector(combVec, topn=5)\n",
+    "            tSim = sum(sim for _, sim in clWords)\n",
+    "            randVal = np.random.random() * tSim\n",
+    "            cumulativeSim = 0\n",
+    "            for word, sim in clWords:\n",
+    "                cumulativeSim += sim\n",
+    "                if cumulativeSim > randVal:\n",
+    "                    if word not in generated:\n",
+    "                        generated.append(word)\n",
+    "                    break\n",
+    "    return ' '.join(generated[:4])\n",
+    "\n",
+    "input_ = np.random.choice(sentences)\n",
+    "generated = GenerateSentence(input_)\n",
+    "\n",
+    "print(f\"Generated: {generated}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551c3416-bbc8-4bca-bb77-ef3622e1dade",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}