notebooks/qnlp-experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from discopy import Ty, Word\n",
    "\n",
    "s, n = Ty('s'), Ty('n')\n",
    "Alice = Word('Alice', n)\n",
    "loves = Word('loves', n.r @ s @ n.l)\n",
    "Bob =  Word('Bob', n)\n",
    "who = Word('who', n.r @ n @ s.l @ n)\n",
    "is_rich = Word('is rich', n.r @ s)\n",
    "\n",
    "vocab = [Alice, loves, Bob, who, is_rich]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brute force search for grammatical sentences:\n",
      "Alice is rich.\n",
      "Bob is rich.\n",
      "Alice loves Alice.\n",
      "Alice loves Bob.\n",
      "Bob loves Alice.\n",
      "Bob loves Bob.\n",
      "Alice who is rich is rich.\n",
      "Bob who is rich is rich.\n",
      "Alice who loves Alice is rich.\n",
      "Alice who loves Bob is rich.\n",
      "\n",
      "8.07 seconds to generate 10 sentences.\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "from discopy.grammar import brute_force\n",
    "\n",
    "gen, n_sentences = brute_force(*vocab), 10\n",
    "sentences, parsing = list(), dict()\n",
    "\n",
    "print(\"Brute force search for grammatical sentences:\")\n",
    "\n",
    "start = time()\n",
    "for i in range(n_sentences):\n",
    "    diagram = next(gen)\n",
    "    sentence = ' '.join(str(w)\n",
    "        for w in diagram.boxes if isinstance(w, Word)) + '.'\n",
    "    sentences.append(sentence)\n",
    "    parsing.update({sentence: diagram})\n",
    "    print(sentence)\n",
    "\n",
    "print(\"\\n{:.2f} seconds to generate {} sentences.\".format(time() - start, n_sentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagram for 'Alice who loves Bob is rich.':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from discopy.grammar import draw\n",
    "print(\"Diagram for 'Alice who loves Bob is rich.':\")\n",
    "draw(parsing['Alice who loves Bob is rich.'], draw_types=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit for the GHZ state, used as ansatz for \"who\":\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from discopy.circuit import Circuit, sqrt, Ket, H, Rx, CX, SWAP\n",
    "\n",
    "GHZ = sqrt(2) @ Ket(0, 0, 0)\\\n",
    "    >> Circuit.id(1) @ H @ Circuit.id(1)\\\n",
    "    >> Circuit.id(1) @ CX\\\n",
    "    >> (SWAP >>  CX) @ Circuit.id(1)\n",
    "\n",
    "print(\"Circuit for the GHZ state, used as ansatz for \\\"who\\\":\")\n",
    "GHZ.draw(aspect='auto', draw_types=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit for 'Alice who loves Bob is rich.':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from discopy import CircuitFunctor\n",
    "\n",
    "def intransitive_ansatz(phase):\n",
    "    return Ket(0) >> Rx(phase)\n",
    "\n",
    "def transitive_ansatz(phase):\n",
    "    return sqrt(2) @ Ket(0, 0) >> H @ Rx(phase) >> CX\n",
    "\n",
    "F = lambda params: CircuitFunctor(\n",
    "    ob={s: 0, n: 1},\n",
    "    ar={Alice: Ket(0),\n",
    "        loves: transitive_ansatz(params[0]),\n",
    "        Bob: Ket(1),\n",
    "        who: GHZ,\n",
    "        is_rich: intransitive_ansatz(params[1])})\n",
    "\n",
    "params0 = np.array([0.5, 1.])\n",
    "print(\"Circuit for 'Alice who loves Bob is rich.':\")\n",
    "circuit = F(params0)(parsing['Alice who loves Bob is rich.'])\n",
    "circuit.draw(figsize=(10, 8), aspect='auto', draw_types=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tket::Circuit qubits=8, gates=16>:\n",
      "Rx(0*PI) q[1];\n",
      "Rx(1*PI) q[2];\n",
      "X q[3];\n",
      "H q[5];\n",
      "H q[7];\n",
      "CX q[5], q[2];\n",
      "CX q[7], q[4];\n",
      "CX q[7], q[0];\n",
      "CX q[2], q[3];\n",
      "CX q[4], q[5];\n",
      "CX q[0], q[1];\n",
      "H q[2];\n",
      "H q[4];\n",
      "CX q[6], q[7];\n",
      "H q[0];\n",
      "H q[6];\n",
      "\n",
      "post selection:\n",
      "{6: 0, 7: 0, 4: 0, 5: 0, 2: 0, 3: 0, 0: 0, 1: 0}\n",
      "\n",
      "scalar:\n",
      "7.999999046325684\n",
      "\n",
      "qiskit circuit:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">                            ┌───┐     ┌───┐\n",
       "q_0: |0>────────────────────┤ X ├──■──┤ H ├\n",
       "        ┌───────┐           └─┬─┘┌─┴─┐└───┘\n",
       "q_1: |0>┤ Rx(0) ├─────────────┼──┤ X ├─────\n",
       "        ├───────┴┐┌───┐       │  ├───┤     \n",
       "q_2: |0>┤ Rx(pi) ├┤ X ├──■────┼──┤ H ├─────\n",
       "        └─┬───┬──┘└─┬─┘┌─┴─┐  │  └───┘     \n",
       "q_3: |0>──┤ X ├─────┼──┤ X ├──┼────────────\n",
       "          └───┘     │  ├───┤  │       ┌───┐\n",
       "q_4: |0>────────────┼──┤ X ├──┼────■──┤ H ├\n",
       "          ┌───┐     │  └─┬─┘  │  ┌─┴─┐└───┘\n",
       "q_5: |0>──┤ H ├─────■────┼────┼──┤ X ├─────\n",
       "          └───┘          │    │  └───┘┌───┐\n",
       "q_6: |0>─────────────────┼────┼────■──┤ H ├\n",
       "          ┌───┐          │    │  ┌─┴─┐└───┘\n",
       "q_7: |0>──┤ H ├──────────■────■──┤ X ├─────\n",
       "          └───┘                  └───┘     </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x12d1bea50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytket.qiskit import tk_to_qiskit\n",
    "\n",
    "tk_circ = circuit.to_tk()\n",
    "print(\"{}:\\n{}\\n\".format(tk_circ, '\\n'.join(map(str, tk_circ))))\n",
    "print(\"post selection:\\n{}\\n\".format(tk_circ.post_selection))\n",
    "print(\"scalar:\\n{}\\n\".format(tk_circ.scalar))\n",
    "print(\"qiskit circuit:\")\n",
    "tk_to_qiskit(tk_circ).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giovannidefelice/.pyenv/versions/3.7.4/lib/python3.7/site-packages/qiskit/providers/models/backendconfiguration.py:337: UserWarning: `dt` and `dtm` now have units of seconds(s) rather than nanoseconds(ns).\n",
      "  warnings.warn('`dt` and `dtm` now have units of seconds(s) rather '\n"
     ]
    }
   ],
   "source": [
    "from qiskit import IBMQ\n",
    "from qiskit.providers.aer import noise\n",
    "\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(group='open')\n",
    "device = provider.get_backend('ibmq_singapore')\n",
    "properties = device.properties()\n",
    "noise_model = noise.device.basic_device_noise_model(properties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytket.backends.ibm import AerBackend, IBMQBackend\n",
    "from discopy.tk_interface import tensor_from_counts\n",
    "\n",
    "# backend = IBMQ singapore\n",
    "singapore = IBMQBackend('ibmq_singapore', hub='ibmq')\n",
    "\n",
    "def evaluate(params, sentences, backend=singapore, n_shots=2**10, seed=42):\n",
    "    circuits = [F(params)(parsing[s]).to_tk().measure_all() for s in sentences]\n",
    "    list(map(backend.default_compilation_pass.apply, circuits))\n",
    "    backend.process_circuits(circuits, n_shots=n_shots, seed=seed)\n",
    "    return [tensor_from_counts(\n",
    "        backend.get_counts(c, n_shots=n_shots),\n",
    "        c.post_selection, c.scalar).array for c in circuits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.067 seconds to compute the corpus.\n",
      "\n",
      "True sentences:\n",
      "Alice is rich. (0.980)\n",
      "Alice loves Bob. (1.008)\n",
      "Bob loves Alice. (0.930)\n",
      "Alice who is rich is rich. (0.922)\n",
      "Alice who loves Bob is rich. (1.437)\n",
      "\n",
      "False sentences:\n",
      "Bob is rich. (0.000)\n",
      "Alice loves Alice. (0.000)\n",
      "Bob loves Bob. (0.000)\n",
      "Bob who is rich is rich. (0.000)\n",
      "Alice who loves Alice is rich. (0.000)\n",
      "\n",
      "Maybe sentences:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "start = time()\n",
    "corpus = dict(zip(sentences, evaluate(params0, sentences, backend=AerBackend())))\n",
    "print(\"{:.3f} seconds to compute the corpus.\\n\".format(time() - start))\n",
    "\n",
    "delta = .1\n",
    "\n",
    "print(\"True sentences:\\n{}\\n\".format('\\n'.join(\"{} ({:.3f})\".format(sentence, scalar)\n",
    "    for sentence, scalar in corpus.items() if scalar > .5 + delta)))\n",
    "print(\"False sentences:\\n{}\\n\".format('\\n'.join(\"{} ({:.3f})\".format(sentence, scalar)\n",
    "    for sentence, scalar in corpus.items() if scalar < .5 - delta)))\n",
    "print(\"Maybe sentences:\\n{}\".format('\\n'.join(\"{} ({:.3f})\".format(sentence, scalar)\n",
    "    for sentence, scalar in corpus.items() if .5 - delta <= scalar <= .5 + delta)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set:\n",
      "Alice who is rich is rich.\n",
      "Bob is rich.\n",
      "Alice is rich.\n",
      "Alice loves Bob.\n",
      "Alice loves Alice.\n",
      "\n",
      "Testing set:\n",
      "Bob loves Bob.\n",
      "Alice who loves Alice is rich.\n",
      "Bob loves Alice.\n",
      "Alice who loves Bob is rich.\n",
      "Bob who is rich is rich.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "sentence_train, sentence_test = train_test_split(sentences, test_size=0.5, random_state=237)\n",
    "\n",
    "print(\"Training set:\\n{}\\n\".format('\\n'.join(sentence_train)))\n",
    "print(\"Testing set:\\n{}\".format('\\n'.join(map(str, sentence_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def loss(params, sentences=sentence_train):\n",
    "    return - np.mean(np.array([\n",
    "        (corpus[sentence] - scalar) ** 2\n",
    "        for sentence, scalar in zip(sentences, evaluate(params, sentences))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random initialisation: [0.2018591  0.83909858]\n",
      "\n",
      "Is Alice who loves Bob rich?\n",
      "Job Status: job has successfully run\n",
      "No, she isn't.\n"
     ]
    }
   ],
   "source": [
    "from random import random, seed; seed(337)\n",
    "\n",
    "params = np.array([random(), random()])\n",
    "\n",
    "print(\"Random initialisation: {}\".format(params))\n",
    "\n",
    "print(\"\\nIs Alice who loves Bob rich?\")\n",
    "print(\"Yes, she is.\"\n",
    "      if evaluate(params, ['Alice who loves Bob is rich.'])[0] > .5 + delta\n",
    "      else \"No, she isn't.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 1 (56 seconds since start): [0.20067549 0.84028219]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 2 (116 seconds since start): [0.38669025 1.02629694]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 3 (175 seconds since start): [0.41269714 1.00029004]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 4 (239 seconds since start): [0.4369038 1.0244967]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 5 (295 seconds since start): [0.45975668 1.00164382]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 6 (352 seconds since start): [0.47758726 1.01947441]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 7 (409 seconds since start): [0.50624273 0.99081894]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 8 (468 seconds since start): [0.46607072 1.03099095]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 9 (524 seconds since start): [0.50585482 0.99120684]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Epoch 10 (582 seconds since start): [0.50845427 0.99380629]\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     fun: -0.0062538115\n",
       " message: 'terminated after reaching max number of iterations'\n",
       "    nfev: 20\n",
       "     nit: 10\n",
       " success: True\n",
       "       x: array([0.50845427, 0.99380629])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import noisyopt\n",
    "\n",
    "i, start = 0, time()\n",
    "\n",
    "def callback(params):\n",
    "    global i\n",
    "    i += 1\n",
    "    print(\"Epoch {} ({:.0f} seconds since start): {}\".format(i, time() - start, params))\n",
    "\n",
    "result = noisyopt.minimizeSPSA(\n",
    "    loss, params, paired=False, callback=callback, niter=10, a=2)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Job Status: job has successfully run\n",
      "Testing loss: -0.08247067034244537\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing loss: {}\".format(loss(result.x, sentences=sentence_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is Alice who loves Bob rich?\n",
      "Job Status: job has successfully run\n",
      "Yes, she is.\n"
     ]
    }
   ],
   "source": [
    "print(\"Is Alice who loves Bob rich?\")\n",
    "print(\"Yes, she is.\"\n",
    "      if evaluate(result.x, ['Alice who loves Bob is rich.'])[0] > .5 + delta\n",
    "      else \"No, she isn't.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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