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   "source": [
    "# Meyer Penny Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyquil.quil as pq\n",
    "from pyquil import api\n",
    "from pyquil.gates import I, H, X\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meyer_penny_program():\n",
    "    \"\"\"\n",
    "    Returns the program to simulate the Meyer-Penny Game\n",
    "    The full description is available in ../docs/source/exercises.rst\n",
    "\n",
    "    :return: pyQuil Program\n",
    "    \"\"\"\n",
    "    prog = pq.Program()\n",
    "    ro = prog.declare(\"ro\", memory_size=2)\n",
    "    picard_register = ro[1]\n",
    "    answer_register = ro[0]\n",
    "\n",
    "    then_branch = pq.Program(X(0))\n",
    "    else_branch = pq.Program(I(0))\n",
    "\n",
    "    # Prepare Qubits in Heads state or superposition, respectively\n",
    "    prog.inst(X(0), H(1))\n",
    "    # Q puts the coin into a superposition\n",
    "    prog.inst(H(0))\n",
    "    # Picard makes a decision and acts accordingly\n",
    "    prog.measure(1, picard_register)\n",
    "    prog.if_then(picard_register, then_branch, else_branch)\n",
    "    # Q undoes his superposition operation\n",
    "    prog.inst(H(0))\n",
    "    # The outcome is recorded into the answer register\n",
    "    prog.measure(0, answer_register)\n",
    "\n",
    "    return prog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of games: 10\n",
      "Q's winning average: 1.0\n",
      "Picard's flip-decision average: 0.4\n"
     ]
    }
   ],
   "source": [
    "n_trials = 10\n",
    "qvm = api.QVMConnection()\n",
    "outcomes = np.asarray(qvm.run(meyer_penny_program(), [0, 1], trials=n_trials))\n",
    "\n",
    "print(\"Number of games: {}\".format(n_trials))\n",
    "print(\"Q's winning average: {}\".format(outcomes[:, 0].mean()))\n",
    "print(\"Picard's flip-decision average: {}\".format(outcomes[:, 1].mean()))"
   ]
  }
 ],
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