chapter_14.ipynb

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   "source": [
    "# Chapter 14\n",
    "\n",
    "## Section 14.1: Forward Sampling"
   ],
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    "# Import data and modules from previous chapter 10\n",
    "\n",
    "import pandas as pd\n",
    "train = pd.read_csv('./data/train.csv')\n",
    "\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit import ClassicalRegister, QuantumRegister\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.circuit.library import ZGate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from math import asin, sqrt\n",
    "\n",
    "def prob_to_angle(prob):\n",
    "    return 2*asin(sqrt(prob))\n",
    "\n",
    "# the maximum age of a passenger we consider as a child\n",
    "max_child_age = 8\n",
    "\n",
    "# probability of being a child\n",
    "population_child = train[train.Age.le(max_child_age)]\n",
    "p_child = len(population_child)/len(train)\n",
    "\n",
    "# probability of being female\n",
    "population_female = train[train.Sex.eq(\"female\")]\n",
    "p_female = len(population_female)/len(train)\n",
    "\n",
    "pop_first = train[train.Pclass.eq(1)]\n",
    "surv_first =  round(len(pop_first[pop_first.Survived.eq(1)])/len(pop_first), 2)\n",
    "p_first = round(len(pop_first)/len(train), 2)\n",
    "\n",
    "pop_second = train[train.Pclass.eq(2)]\n",
    "surv_second =  round(len(pop_second[pop_second.Survived.eq(1)])/len(pop_second), 2)\n",
    "p_second = round(len(pop_second)/len(train), 2)\n",
    "\n",
    "pop_third = train[train.Pclass.eq(3)]\n",
    "surv_third =  round(len(pop_third[pop_third.Survived.eq(1)])/len(pop_third), 2)\n",
    "p_third = round(len(pop_third)/len(train), 2)\n",
    "\n",
    "\n",
    "# positions of the qubits\n",
    "QPOS_ISCHILD = 0\n",
    "QPOS_SEX = 1\n",
    "QPOS_NORM = 2\n",
    "QPOS_FIRST = 3\n",
    "QPOS_SECOND = 4\n",
    "QPOS_THIRD = 5\n",
    "QPOS_SURV = 6\n",
    "QUBITS = 7\n",
    "\n",
    "\n",
    "trained_params = {\n",
    "    'p_norm_cms': 0.6021334301303094,\n",
    "    'p_norm_cmd': 0.07088902981523437,\n",
    "    'p_norm_cfs': 0.9904336919724537,\n",
    "    'p_norm_cfd': 0.8392179490424515,\n",
    "    'p_norm_ams': 0.49195927424087027,\n",
    "    'p_norm_amd': 0.04654642501038004,\n",
    "    'p_norm_afs': 0.9978526500251851,\n",
    "    'p_norm_afd': 0.9590619707414763\n",
    "}\n",
    "\n",
    "def prepare_data(passengers, params):\n",
    "    \"\"\"\n",
    "    params = {\n",
    "        'p_norm_cms': 0.45,\n",
    "        'p_norm_cmd': 0.46,\n",
    "        'p_norm_cfs': 0.47,\n",
    "        'p_norm_cfd': 0.48,\n",
    "        'p_norm_ams': 0.49,\n",
    "        'p_norm_amd': 0.51,\n",
    "        'p_norm_afs': 0.52,\n",
    "        'p_norm_afd': 0.53,\n",
    "    }\n",
    "    \"\"\"\n",
    "    # is the passenger a child?\n",
    "    passengers['IsChild'] = passengers['Age'].map(lambda age: 0 if age > max_child_age else 1)\n",
    "\n",
    "    # the probability of favored by norm given Age, Sex, and Survival\n",
    "    passengers['Norm'] = list(map(\n",
    "        lambda item: params['p_norm_{}{}{}'.format(\n",
    "            'a' if item[0] == 0 else 'c',\n",
    "            item[1][0],\n",
    "            'd' if item[2] == 0 else 's'\n",
    "        )],\n",
    "        list(zip(passengers['IsChild'], passengers['Sex'], passengers['Survived']))\n",
    "    ))\n",
    "    return passengers\n",
    "\n",
    "def calculate_norm_params(passengers):\n",
    "    # the different populations in our data\n",
    "    pop_children = passengers[passengers.IsChild.eq(1)]\n",
    "    pop_adults = passengers[passengers.IsChild.eq(0)]\n",
    "\n",
    "    # combinations of being a child and gender\n",
    "    pop_am = pop_adults[pop_adults.Sex.eq('male')]\n",
    "    pop_af = pop_adults[pop_adults.Sex.eq('female')]\n",
    "    pop_cm = pop_children[pop_children.Sex.eq('male')]\n",
    "    pop_cf = pop_children[pop_children.Sex.eq('female')]\n",
    "\n",
    "    norm_params = {\n",
    "        'p_norm_am': pop_am.Norm.sum() /  len(pop_am),\n",
    "        'p_norm_af': pop_af.Norm.sum() /  len(pop_af),\n",
    "        'p_norm_cm': pop_cm.Norm.sum() /  len(pop_cm),\n",
    "        'p_norm_cf': pop_cf.Norm.sum() /  len(pop_cf),\n",
    "    }\n",
    "\n",
    "    return norm_params\n",
    "\n",
    "def calculate_surv_params(passengers):\n",
    "    # all survivors\n",
    "    survivors = passengers[passengers.Survived.eq(1)]\n",
    "    \n",
    "    # weight the passenger\n",
    "    def weight_passenger(norm, pclass):\n",
    "        return lambda passenger: (passenger[0] if norm else 1-passenger[0]) * (1 if passenger[1] == pclass else 0)\n",
    "\n",
    "    # calculate the probability to survive\n",
    "    def calc_prob(norm, pclass):\n",
    "        return sum(list(map(\n",
    "            weight_passenger(norm, pclass),\n",
    "            list(zip(survivors['Norm'], survivors['Pclass']))\n",
    "        ))) / sum(list(map(\n",
    "            weight_passenger(norm, pclass), \n",
    "            list(zip(passengers['Norm'], passengers['Pclass']))\n",
    "        )))\n",
    "    \n",
    "    surv_params = {\n",
    "        'p_surv_f1': calc_prob(True, 1),\n",
    "        'p_surv_f2': calc_prob(True, 2),\n",
    "        'p_surv_f3': calc_prob(True, 3),\n",
    "        'p_surv_u1': calc_prob(False, 1),\n",
    "        'p_surv_u2': calc_prob(False, 2),\n",
    "        'p_surv_u3': calc_prob(False, 3)\n",
    "    }\n",
    "\n",
    "    return surv_params\n",
    "\n",
    "def ccry(qc, theta, control1, control2, controlled):\n",
    "    qc.cry(theta/2, control2, controlled)\n",
    "    qc.cx(control1, control2)\n",
    "    qc.cry(-theta/2, control2, controlled)\n",
    "    qc.cx(control1, control2)\n",
    "    qc.cry(theta/2, control1, controlled)\n",
    "\n",
    "def as_pqc(cnt_quantum, with_qc, cnt_classical=1, shots=1, hist=False, measure=False):\n",
    "    # Prepare the circuit with qubits and a classical bit to hold the measurement\n",
    "    qr = QuantumRegister(cnt_quantum)\n",
    "    cr = ClassicalRegister(cnt_classical)\n",
    "    qc = QuantumCircuit(qr, cr) if measure else QuantumCircuit(qr)\n",
    "\n",
    "    with_qc(qc, qr=qr, cr=cr)\n",
    "    \n",
    "    results = execute(\n",
    "        qc,\n",
    "        Aer.get_backend('statevector_simulator') if measure is False else Aer.get_backend('qasm_simulator'),\n",
    "        shots=shots\n",
    "    ).result().get_counts()\n",
    "    \n",
    "    return plot_histogram(results, figsize=(12,4)) if hist else results\n",
    "\n",
    "\n",
    "def apply_ischild_sex(qc):\n",
    "    # set the marginal probability of isChild\n",
    "    qc.ry(prob_to_angle(p_child), QPOS_ISCHILD)\n",
    "\n",
    "    # set the marginal probability of Sex\n",
    "    qc.ry(prob_to_angle(p_female), QPOS_SEX)\n",
    "\n",
    "\n",
    "def apply_norm(qc, norm_params):\n",
    "    \"\"\"\n",
    "    norm_params = {\n",
    "        'p_norm_am': 0.25,\n",
    "        'p_norm_af': 0.35,\n",
    "        'p_norm_cm': 0.45,\n",
    "        'p_norm_cf': 0.55\n",
    "    }\n",
    "    \"\"\"\n",
    "\n",
    "    # set the conditional probability of Norm given adult/male\n",
    "    qc.x(QPOS_ISCHILD)\n",
    "    qc.x(QPOS_SEX)\n",
    "    ccry(qc, prob_to_angle(\n",
    "        norm_params['p_norm_am']\n",
    "    ),QPOS_ISCHILD, QPOS_SEX, QPOS_NORM)\n",
    "    qc.x(QPOS_ISCHILD)\n",
    "    qc.x(QPOS_SEX)\n",
    "\n",
    "    # set the conditional probability of Norm given adult/female\n",
    "    qc.x(QPOS_ISCHILD)\n",
    "    ccry(qc, prob_to_angle(\n",
    "        norm_params['p_norm_af']\n",
    "    ),QPOS_ISCHILD, QPOS_SEX, QPOS_NORM)\n",
    "    qc.x(QPOS_ISCHILD)\n",
    "\n",
    "    # set the conditional probability of Norm given child/male\n",
    "    qc.x(QPOS_SEX)\n",
    "    ccry(qc, prob_to_angle(\n",
    "        norm_params['p_norm_cm']\n",
    "    ),QPOS_ISCHILD, QPOS_SEX, QPOS_NORM)\n",
    "    qc.x(QPOS_SEX)\n",
    "\n",
    "    # set the conditional probability of Norm given child/female\n",
    "    ccry(qc, prob_to_angle(\n",
    "        norm_params['p_norm_cf']\n",
    "    ),QPOS_ISCHILD, QPOS_SEX, QPOS_NORM)\n",
    "\n",
    "def apply_class(qc):\n",
    "    # set the marginal probability of Pclass=1st\n",
    "    qc.ry(prob_to_angle(p_first), QPOS_FIRST)\n",
    "\n",
    "    qc.x(QPOS_FIRST)\n",
    "    # set the marginal probability of Pclass=2nd\n",
    "    qc.cry(prob_to_angle(p_second/(1-p_first)), QPOS_FIRST, QPOS_SECOND)\n",
    "\n",
    "    # set the marginal probability of Pclass=3rd    \n",
    "    qc.x(QPOS_SECOND)\n",
    "    ccry(qc, prob_to_angle(p_third/(1-p_first-p_second)), QPOS_FIRST, QPOS_SECOND, QPOS_THIRD)\n",
    "    qc.x(QPOS_SECOND)\n",
    "    qc.x(QPOS_FIRST)\n",
    "\n",
    "def apply_survival(qc, surv_params):    \n",
    "    \"\"\"\n",
    "    surv_params = {\n",
    "        'p_surv_f1': 0.3,\n",
    "        'p_surv_f2': 0.4,\n",
    "        'p_surv_f3': 0.5,\n",
    "        'p_surv_u1': 0.6,\n",
    "        'p_surv_u2': 0.7,\n",
    "        'p_surv_u3': 0.8\n",
    "    }\n",
    "    \"\"\"\n",
    "\n",
    "    # set the conditional probability of Survival given unfavored by norm\n",
    "    qc.x(QPOS_NORM)\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_u1']\n",
    "    ),QPOS_NORM, QPOS_FIRST, QPOS_SURV)\n",
    "\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_u2']\n",
    "    ),QPOS_NORM, QPOS_SECOND, QPOS_SURV)\n",
    "\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_u3']\n",
    "    ),QPOS_NORM, QPOS_THIRD, QPOS_SURV)\n",
    "    qc.x(QPOS_NORM)\n",
    "\n",
    "    # set the conditional probability of Survival given favored by norm\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_f1']\n",
    "    ),QPOS_NORM, QPOS_FIRST, QPOS_SURV)\n",
    "\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_f2']\n",
    "    ),QPOS_NORM, QPOS_SECOND, QPOS_SURV)\n",
    "\n",
    "    ccry(qc, prob_to_angle(\n",
    "        surv_params['p_surv_f3']\n",
    "    ),QPOS_NORM, QPOS_THIRD, QPOS_SURV)\n",
    "\n"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "# Listing 14.1: Preparing the trained QBN\n",
    "def get_trained_qbn(passengers, params, hist=True):\n",
    "\n",
    "    prepared_passengers = prepare_data(passengers, params)\n",
    "    norm_params = calculate_norm_params(prepared_passengers)\n",
    "    surv_params = calculate_surv_params(prepared_passengers)\n",
    "\n",
    "    def qbn_titanic(): \n",
    "        def circuit(qc, qr=None, cr=None):\n",
    "            apply_ischild_sex(qc)\n",
    "            apply_norm(qc, norm_params)\n",
    "            apply_class(qc)\n",
    "            apply_survival(qc, surv_params)\n",
    "\n",
    "            qc.measure(QPOS_SURV, cr[0])\n",
    "\n",
    "        return as_pqc(QUBITS, circuit, hist=hist, cnt_classical=1, measure=True, shots=1000)\n",
    "    return qbn_titanic()"
   ]
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\n"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "# Listing 14.2: Use the QBN for forward sampling\n",
    "get_trained_qbn(train, trained_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Section 14.2: Bayesian Rejection Sampling\n",
    "def get_trained_qbn_norm(passengers, params):\n",
    "\n",
    "    prepared_passengers = prepare_data(passengers, params)\n",
    "    norm_params = calculate_norm_params(prepared_passengers)\n",
    "    surv_params = calculate_surv_params(prepared_passengers)\n",
    "\n",
    "    def qbn_titanic(): \n",
    "        def circuit(qc, qr=None, cr=None):\n",
    "            apply_ischild_sex(qc)\n",
    "            apply_norm(qc, norm_params)\n",
    "            apply_class(qc)\n",
    "            apply_survival(qc, surv_params)\n",
    "\n",
    "            qc.measure(QPOS_ISCHILD, cr[0])\n",
    "            qc.measure(QPOS_SEX, cr[1])\n",
    "            qc.measure(QPOS_NORM, cr[2])\n",
    "\n",
    "        return as_pqc(QUBITS, circuit, hist=False, cnt_classical=3, measure=True, shots=10000)\n",
    "    return qbn_titanic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'000': 5333,\n",
       " '001': 331,\n",
       " '010': 31,\n",
       " '011': 10,\n",
       " '100': 736,\n",
       " '101': 75,\n",
       " '110': 3298,\n",
       " '111': 186}"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "# Listing 14.4: Obtain the measurements\n",
    "norm_mes = get_trained_qbn_norm(train, trained_params)\n",
    "norm_mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Chance to be favored by norm\nFemale children: 0.95 \nFemale adults: 0.99 \nMale children: 0.18 \nMale adults: 0.12 \n"
     ]
    }
   ],
   "source": [
    "# Listing 14.5: Calculate the conditional probabilities of being favored by a norm\n",
    "print(\"Chance to be favored by norm\")\n",
    "print(\"Female children: {:.2f} \".format(norm_mes['111']/(norm_mes['111']+norm_mes['011'])))\n",
    "print(\"Female adults: {:.2f} \".format(norm_mes['110']/(norm_mes['110']+norm_mes['010'])))\n",
    "print(\"Male children: {:.2f} \".format(norm_mes['101']/(norm_mes['101']+norm_mes['001'])))\n",
    "print(\"Male adults: {:.2f} \".format(norm_mes['100']/(norm_mes['100']+norm_mes['000'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 14.6: Preparing the trained QBN to measure survival\n",
    "def get_trained_qbn_surv(passengers, params):\n",
    "\n",
    "    prepared_passengers = prepare_data(passengers, params)\n",
    "    norm_params = calculate_norm_params(prepared_passengers)\n",
    "    surv_params = calculate_surv_params(prepared_passengers)\n",
    "\n",
    "    def qbn_titanic(): \n",
    "        def circuit(qc, qr=None, cr=None):\n",
    "            apply_ischild_sex(qc)\n",
    "            apply_norm(qc, norm_params)\n",
    "            apply_class(qc)\n",
    "            apply_survival(qc, surv_params)\n",
    "\n",
    "            qc.measure(QPOS_NORM, cr[0])\n",
    "            qc.measure(QPOS_FIRST, cr[1])\n",
    "            qc.measure(QPOS_SECOND, cr[2])\n",
    "            qc.measure(QPOS_THIRD, cr[3])\n",
    "            qc.measure(QPOS_SURV, cr[4])\n",
    "\n",
    "        return as_pqc(QUBITS, circuit, hist=False, cnt_classical=5, measure=True, shots=10000)\n",
    "    return qbn_titanic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'10010': 339,\n",
       " '10011': 957,\n",
       " '10100': 79,\n",
       " '10101': 837,\n",
       " '11000': 247,\n",
       " '11001': 1289,\n",
       " '00010': 1071,\n",
       " '00011': 68,\n",
       " '00100': 1024,\n",
       " '00101': 80,\n",
       " '01000': 2948,\n",
       " '01001': 1061}"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "# Listing 14.7: Obtain the measurements\n",
    "surv_mes = get_trained_qbn_surv(train, trained_params)\n",
    "surv_mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Chance to survive\nFavored 1st: 0.93 \nFavored 2nd: 0.91 \nFavored 3rd: 0.55 \nUnfavored 1st: 0.24 \nUnfavored 2nd: 0.07 \nUnfavored 3rd: 0.08 \n"
     ]
    }
   ],
   "source": [
    "# Listing 14.8: Calculate the conditional probabilities of survival\n",
    "print(\"Chance to survive\")\n",
    "print(\"Favored 1st: {:.2f} \".format(\n",
    "    surv_mes['10011']/(surv_mes['10011']+surv_mes['00011'])))\n",
    "print(\"Favored 2nd: {:.2f} \".format(\n",
    "    surv_mes['10101']/(surv_mes['10101']+surv_mes['00101'])))\n",
    "print(\"Favored 3rd: {:.2f} \".format(\n",
    "    surv_mes['11001']/(surv_mes['11001']+surv_mes['01001'])))\n",
    "print(\"Unfavored 1st: {:.2f} \".format(\n",
    "    surv_mes['10010']/(surv_mes['10010']+surv_mes['00010'])))\n",
    "print(\"Unfavored 2nd: {:.2f} \".format(\n",
    "    surv_mes['10100']/(surv_mes['10100']+surv_mes['00100'])))\n",
    "print(\"Unfavored 3rd: {:.2f} \".format(\n",
    "    surv_mes['11000']/(surv_mes['11000']+surv_mes['01000'])))"
   ]
  },
  {
   "source": [
    "## Section 14.3: Quantum Rejection Sampling"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.94 of female children were favored by a norm \nConsidered 209 samples \nAcceptance of 2.1% of the samples\n"
     ]
    }
   ],
   "source": [
    "# Listing 14.9: Evaluate the effectiveness\n",
    "norm_mes = get_trained_qbn_norm(train, trained_params)\n",
    "print(\"{:.2f} of female children were favored by a norm \".format(\n",
    "    norm_mes['111']/(norm_mes['111']+norm_mes['011'])))\n",
    "print(\"Considered {} samples \".format(\n",
    "    norm_mes['111']+norm_mes['011']))\n",
    "print (\"Acceptance of {:.1f}% of the samples\".format(\n",
    "    (norm_mes['111']+norm_mes['011'])/100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 14.10: The oracle‐function\n",
    "def oracle(qr, draw=False):\n",
    "    # Create a sub-circuit\n",
    "    o_qc = QuantumCircuit(qr)\n",
    "\n",
    "    o_qc.append(ZGate().control(2), [qr[QPOS_ISCHILD], qr[QPOS_SEX], qr[QPOS_NORM]])    \n",
    "    \n",
    "    if draw:\n",
    "        return o_qc.draw()\n",
    "    else:\n",
    "        # We return the oracle as a gate\n",
    "        Oracle = o_qc.to_gate()\n",
    "        Oracle.name = \"oracle\"\n",
    "        return Oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 14.11: The amplifier‐function\n",
    "def amplifier(qr, draw=False):\n",
    "    # Create a sub-circuit\n",
    "    a_qc = QuantumCircuit(qr)\n",
    "\n",
    "    apply_ischild_sex(a_qc) # H\n",
    "    a_qc.x(qr[QPOS_ISCHILD])\n",
    "    a_qc.x(qr[QPOS_SEX])\n",
    "    a_qc.cz(qr[QPOS_ISCHILD], qr[QPOS_SEX])\n",
    "    a_qc.x(qr[QPOS_ISCHILD])\n",
    "    a_qc.x(qr[QPOS_SEX])\n",
    "    apply_ischild_sex(a_qc) # H\n",
    "    \n",
    "    if draw:\n",
    "        return a_qc.draw()\n",
    "    else:\n",
    "        # We return the oracle as a gate\n",
    "        Amplifier = a_qc.to_gate()\n",
    "        Amplifier.name = \"amplifier\"\n",
    "        return Amplifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing 14.12: Prepare the amplified QBN\n",
    "def get_trained_qbn_amp(passengers, params):\n",
    "\n",
    "    prepared_passengers = prepare_data(passengers, params)\n",
    "    norm_params = calculate_norm_params(prepared_passengers)\n",
    "    surv_params = calculate_surv_params(prepared_passengers)\n",
    "\n",
    "    def qbn_titanic(): \n",
    "        def circuit(qc, qr=None, cr=None):\n",
    "        \n",
    "            # amplifier replacing the H-gates\n",
    "            qc.append(amplifier(qr), qr)\n",
    "\n",
    "            # Amplification\n",
    "            qc.append(oracle(qr), qr)\n",
    "            qc.append(amplifier(qr), qr)\n",
    "            qc.append(oracle(qr), qr)\n",
    "            qc.append(amplifier(qr), qr)\n",
    "            \n",
    "            qc.x(QPOS_ISCHILD)\n",
    "            qc.x(QPOS_SEX)\n",
    "            apply_norm(qc, norm_params)\n",
    "            apply_survival(qc, surv_params)\n",
    "            \n",
    "            qc.measure(QPOS_ISCHILD, cr[0])\n",
    "            qc.measure(QPOS_SEX, cr[1])\n",
    "            qc.measure(QPOS_NORM, cr[2])\n",
    "\n",
    "        return as_pqc(QUBITS, circuit, hist=False, cnt_classical=3, measure=True, shots=10000)\n",
    "    return qbn_titanic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.94 of female children were favored by a norm \nConsidered 9834 samples \nAcceptance of 98.3% of the samples\n"
     ]
    }
   ],
   "source": [
    "# Listing 14.13: Evaluate the effectiveness of the amplified QBN\n",
    "amp_mes = get_trained_qbn_amp(train, trained_params)\n",
    "print(\"{:.2f} of female children were favored by a norm \".format(\n",
    "    amp_mes['111']/(amp_mes['111']+amp_mes['011'])))\n",
    "print(\"Considered {} samples \".format(\n",
    "    amp_mes['111']+amp_mes['011']))\n",
    "print (\"Acceptance of {:.1f}% of the samples\".format(\n",
    "    (amp_mes['111']+amp_mes['011'])/100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}