gym-quantcircuit-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gym-QuantCircuit Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A step by step example of using the custom quantum circuit gym environment to create and visualise quantum circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_quantcircuit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The make method creates a completely blank environment that has no functionality yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('quantcircuit-v0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment has an extra initialisation method that allows it to take in arguments that help with customisation. These arguments include the number of desired qubits in the circuit, whether calculations are to be performed with unitary matrices or statevectors, the set of gates used, the physical connectivity of the qubits, and the goal statevector or unitary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_qubits = 3\n",
    "test_goal_state = [0j] * (2**num_qubits - 1) + [1+0j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.var_init(num_qubits,\n",
    "             unitary=False,\n",
    "             gate_group='pauli',\n",
    "             connectivity='fully_connected',\n",
    "             goal_state=test_goal_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above line initialises the environment with a blank circuit with three qubits, using statevectors, using the pauli gate group, with a fully-connected physical architecture, and with a goal statevector of each qubit being flipped. This can be visualised with the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">         \n",
       "q0_0: |0>\n",
       "         \n",
       "q0_1: |0>\n",
       "         \n",
       "q0_2: |0>\n",
       "         </pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x7f077d6d7470>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.plot_connectivity_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantum circuits are generated in this environment with the process of sampling an action from the action space and then applying this sampled action. The action space is automatically created after the number of qubits, gate group, and connectivity are defined. All 'legal' combinations of gates and qubits are generated, and these actions can be sampled by generating an integer representation using .sample()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 1),\n",
       " <bound method cx of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f077d6d7518>>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action = env.sample()\n",
    "env.gate_list[action]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above sampled action represents a CNOT gate with qubit 2 as the control, and qubit 1 as the target. This action can then be taken in the environment. Each action returns an observation of the current state, a reward signal, a boolean that signifies whether the circuit has reached its goal state or unitary, and the fidelity measure of the current state. The observation is provided as the difference between the goal state and the current state. The current reward is sparse, only being attained when the fidelity of the current and goal states rounds to 1 within three digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.]), 0, False, {'fidelity': 0.0})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(action)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This action can be visualised on the current circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">              \n",
       "q0_0: |0>─────\n",
       "         ┌───┐\n",
       "q0_1: |0>┤ X ├\n",
       "         └─┬─┘\n",
       "q0_2: |0>──■──\n",
       "              </pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x7f077756cb00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A larger circuit can be generated by looping over these actions and applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">                   ┌───┐┌───┐          \n",
       "q0_0: |0>──────────┤ X ├┤ H ├──■───────\n",
       "         ┌───┐┌───┐└───┘└───┘  │       \n",
       "q0_1: |0>┤ X ├┤ X ├────────────┼────■──\n",
       "         └─┬─┘└─┬─┘          ┌─┴─┐┌─┴─┐\n",
       "q0_2: |0>──■────■────────────┤ X ├┤ X ├\n",
       "                             └───┘└───┘</pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x7f077755aeb8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for _ in range(5):\n",
    "    env.step(env.sample())\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resetting the circuit clears any gates that have been applied, and resets the goal state/unitary back to the identity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">         \n",
       "q0_0: |0>\n",
       "         \n",
       "q0_1: |0>\n",
       "         \n",
       "q0_2: |0>\n",
       "         </pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x7f0777585240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.reset()\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customisation options\n",
    "##### Gate groups\n",
    "Predefined gate groups include:\n",
    "1. Clifford\n",
    "    * String input: 'clifford'\n",
    "    * Gates: Identity (I), CNOT (CX), Hadamard (H), Phase (S), $\\pi$/8 (T) \n",
    "2. Pauli\n",
    "    * String input: 'pauli'\n",
    "    * Gates: Identity (I), Hadamard (H), X, Z, CNOT (CX)\n",
    "3. IQP (Instantaneous Quantum Polynomial)\n",
    "    * String input: 'IQP'\n",
    "    * Gates: Identity (I), $\\pi$/8 (T), Controlled S (CS)\n",
    "    \n",
    "A custom gate group can also be used by initialising the environment with the gate_group variable set to 'custom'. A list containing the desired gates as qiskit functions must then be passed to the set_gate_group method. The action space with the new custom gates must also be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Environment must be created before this step\n",
    "\n",
    "custom_gate_group = [\n",
    "    env.qcircuit.x,\n",
    "    env.qcircuit.h,\n",
    "    env.qcircuit.t\n",
    "]\n",
    "\n",
    "env.set_gate_group('custom',custom_gate_group)\n",
    "\n",
    "env.gate_list = env._create_gates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((0,),\n",
       "  <bound method x of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((1,),\n",
       "  <bound method x of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((2,),\n",
       "  <bound method x of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((0,),\n",
       "  <bound method h of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((1,),\n",
       "  <bound method h of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((2,),\n",
       "  <bound method h of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((0,),\n",
       "  <bound method t of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((1,),\n",
       "  <bound method t of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>),\n",
       " ((2,),\n",
       "  <bound method t of <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7f07775852e8>>)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.gate_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Qubit Connectivity\n",
    "Three predefined options for qubit connectivity exist within the environment: nearest neighbour, fully connected, and connectivities based on IBMQ's architectures. A custom connectivity can also be used by passing in a binary connectivity array that defines connections between each pair of qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nearest neighbour\n",
    "nnenv = gym.make('quantcircuit-v0')\n",
    "nnenv.var_init(5,connectivity='nearest_neighbour')\n",
    "nnenv.plot_connectivity_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fully Connected\n",
    "fc_env = gym.make('quantcircuit-v0')\n",
    "fc_env.var_init(5,connectivity='fully_connected')\n",
    "fc_env.plot_connectivity_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pMDCQt956ixdffJHbbruNN998U9PNIl7qxIkTHLpoARXAjYA/sIc/Rr1p5369+XIvFhbm7Hgq3fLQ9LJvGzx4MCkpKbz55psMHjxY080iXuDgwYMsXryYJ554gqioKOrXr8/HO3ZQXK2aw+MaACOAEqA3cD+2xVS1gcdKe+HAQHDBEbI6BrIcrrvuOtLT02nSpInRUcSFCgsLeeKJJ/jf//5HcnIyXbp0MTqSiPDHDpLVq1ezevVqUlJSOHr0KGazGbPZjMViITIyksCCAmje/JLruoVAApCMbdVyODADiC3tmwUEwJ49Tj+TWaVbRseOHaNZs2bk5+frDN8qYsGCBYwfP56XXnqJ0aNH630XcbNTp06Rnp5uL9iUlBRCQ0PtBWuxWGjfvj1+fqVM2g4YYDvasSIVZzJB//7w8ceV/0Nc/NIq3bJZu3YtEyZMIC0t7eoPFp+xY8cOBg4cSJs2bXjnnXe0XUzEhY4ePcqaNWtISUlh9erVbNiwgXbt2tkL1mw206hRo7K9WFqa7SzlC06kKrOgIFi5EiIjr/7Ycqru9Ff0UVpEVTW1bt2adevWMWnSJCIiIkhOTiY8PNzoWCJez2q1smvXLnvBrl69mn379tG1a1csFgsvvPACXbt2JTg4uGLfICoKpk+/5CjIqwoKsj3PBYULKt0y0yKqqisgIIA33niD5ORkbr/9dl544QXGjh2r6WaRciguLmbjxo32kk1JScHPz88+gh09ejSdOnWienUn1tKYMbZfExJs+26vNLFrMtkWT02f/sfzXEDTy2XUr18/RowYwYABA4yOIgbKyckhPj6e66+/nnfffVfTzSKXkZ+fz7p16+wFm5qaSosWLezXY81mMy1atHDPD6/p6bazlJcupfDUKQIvrL3z99Pt29d2P10XjXDPU+mWUZs2bViyZAk33nij0VHEYEVFRUyePJmvv/6a5ORkIiIijI4kYrh9+/bZC3b16tXs2LGD8PBw+/XY2NhYwlyw77U8Tu/fz3PNm/PyoEFUy8+37cPt2BFGjHD6KuXLUemWwalTpwgNDSU/P58aNWoYHUc8xKJFixg3bhzPPfcc48aN03SzVBlnz55l69atDtdjT5486TCKDQ8Pp2bNmkZHdfDTTz/Rv39/tm3bZlgGXdMtg5ycHJo3b67CFQcDBw6kS5cuDBo0iOXLl/Pvf/+bOnXqGB1LxOlOnjxJamqqvWTXrl1L/fr1sVgs3HLLLTz33HO0adPG43/w9IS1OSrdMvCEN0o80w033MCaNWtISEggIiKChQsXEunia0Iirpabm+uw4GnLli107NgRs9nMI488QlJSEg0aNLj6C3kYTzg/X6VbBp7wRonnqlmzJv/617/o0aMHffv25ZlnnmH8+PEe/1O/CNi27mzfvt3hemxubi5xcXGYzWZeeeUVoqKiCAoKMjpqpWVnZ9O7d29DM6h0yyA7O5tbb73V6Bji4e677z77dPOKFSuYO3euppvF45w+fZqMjAyHkWytWrXs12MnTpzITTfdRLWLzi72BVlZWTz2WKknLbuNFlKVQWRkJK+//jpdu3Y1Oop4gVOnTjFlyhS+/PJLFixYQHR0tNGRpAo7duwYa9eutS94yszMpHXr1g6nPFWF8+StVishISHs27fP0K1+Kt2rKCkpsb9RGrVIeXzyySeMHj2av/71r0yYMEHTzeJyVquV3bt3O4xid+/eTXR0tL1gY2JiCAkJMTqq2+3bt4/o6GgOHDhgaA5NL1/F/v37CQ4OVuFKuQ0YMIDOnTs7TDcbvU9RfMuZM2fYvHmzw/XYkpISe8GOGjWKm2++GX9/f6OjGs5T1uaodK8iKytLK5elwlq1asXq1auZOnUq4eHhLFy4UNPNUmG///4769atsxfs+vXradq0KWazmTvvvJO///3vtGrVSrMqpfCU8/NVulfhKW+UeK+aNWsyc+ZMevTowV133cW0adOYOHGi/mGUqzpw4IDDARTZ2dl06dIFi8XChAkTiIuL45prrjE6plfwlAGUSvcqtEdXnKV///4O083vvfcedevWNTqWeIiSkhJ++uknh+uxeXl59lXFs2bNIiIigoCAAKOjeqXs7Gz69+9vdAyV7tVkZWV5xBslvqFly5YO080LFiwgJibG6FhigKKiItLS0uwFu2bNGurWrYvFYqF79+48/fTTtG3btvQbtEu5ecoASquXr6Jhw4akpqbStGlTo6OIj1myZAmPPvooU6dO5YknntB0s487cuQIKSkp9pHspk2buOmmmxzOK77uuuuMjumT8vLyaNKkCfn5+Yb/PVPpXsHx48dp2rSpR7xR4pt2797NoEGDaNCgAUlJSZpu9hFWq5WcnByH67EHDx4kNjbWXrLR0dHUqlXL6KhVwvr16xk3bhzp6elGR9H08pVkZ2fTtm1bFa64TIsWLfjhhx+YNm0a4eHhzJ8/n9jYWKNjSTkVFxezYcMGe8GmpKRQo0YN+wEUjz32GB07dvTJU568gacsogKV7hV5yjUA8W01atRgxowZ9OjRg3vuuYcpU6YwadIkXcvzYHl5efZTnlJSUkhPT6dVq1ZYLBbuu+8+Zs6cSbNmzYyOKed40i4Ule4VeMpmaqka+vXrx80338ygQYNYuXIlSUlJ2g7iIfbs2eNwAMXOnTuJiorCbDYzdepUYmJidICOB8vOzmbYsGFGxwBUuleUnZ3Ngw8+aHQMqUKaN2/OqlWrePrpp+3TzXFxcUbHqlLOnj3Lli1bHEr29OnT9muxw4cPp3Pnzrq/thfxpOllLaS6grZt2/Lpp59y4403Gh1FqqAvvviCUaNGkZCQwOTJkzXd7CInTpwgNTXVfj123bp1NGrUyGFV8Q033KC1HV7q9OnThISEkJ+f7xE/KKl0L+P8G5WXl0fNmjWNjiNV1J49exg0aBB169bl/fff59prrzU6ktc7dOiQwwEUW7du5eabb7YXbFxcHPXq1TM6pjhJVlYW99xzD9u2bTM6CqDp5cvKycmhWbNmKlwxVLNmzVi1ahV//etf7dPNZrPZ6Fhew2q1kp2d7TBV/NtvvxEXF4fFYmHGjBlERkYSGBhodFRxEU9bm6PSvQytXBZP4e/vz6uvvkqPHj249957eeKJJ5gyZYqmm0tx6tQp0tPTHUayoaGh9lHslClTaN++vf63q0I8aeUyqHQvy9N+OhK58847SUtL4/7772flypXMmzevyk83Hz16lDVr1thLduPGjbRr1w6z2cywYcN48803adSokdExxUBZWVn07t3b6Bh2Kt3LyM7O9qg3SgSgadOmrFixgmeeeYYuXbowf/58LBaL0bHcwmq18vPPPzscQLF37166du2KxWLhxRdfpGvXrtSuXdvoqOJBsrOzGTdunNEx7LSQ6jIiIyOZPXu2DqMXj7V06VJGjhzJxIkTefLJJ31uyvTMmTNs3LjR4Xqsn5+f/ZQns9lMp06dqF5dYwcpndVqJSQkhL1793rMPmqVbik88Y0SKc3evXsZPHgwwcHBzJs3z6tX3ebn5zvcoD01NZUWLVrYC9ZisdC8eXNt3ZEy27dvH1FRURw8eNDoKHb6EbEU+/fvp3bt2ipc8XhNmzZl+fLlPPfcc4SHh/PRRx/RrVu3yz8hNxeSkmDzZsjLg9BQ6NQJHnoI3FzY+/btcxjF7tixg4iICMxmM5MnTyY2NpawsDC3ZhLf4mmLqEClWyotohJv4u/vT2JiIt27d2fgwIE8/vjjTJ061XG6OS0NEhNh2TLbx0VFf3ztk0/g+eehTx+YNg2iopye8ezZs2zdutVhVfGJEyfsI9ghQ4YQHh7uEYcXiO/wpJOozlPplsITfzoSuZo+ffqQnp7O4MGDWblyJR988IFtunnOHEhIgMJCKO1qUmGh7dclS+Cbb2D6dBgzplJZTp486XCD9rVr11KvXj0sFgu9e/fmueeeo02bNpoqFpfyxH/LVbql8MSfjkTKokmTJg7Tzf8bOJA2b70FJ09e/clWq+1xCQm2j8tRvLm5uQ43aN+yZQsdO3bEYrHw8MMPk5SURP369Sv4pxKpmOzsbP785z8bHcOBFlKV4pZbbuGpp57iT3/6k9FRRCpszf/7f3SeOJGgCz43CkgB9gI1ga7Aq0CHi58cFAQrV0Jk5CWva7Va2b59u8MN2nNzc4mLi7NPF0dFRREUFHTJc0XcqVGjRqxfv56mTZsaHcVOpVsKT3yjRMptwACsS5ZguuCvuAmIwVay3wG7gcZADhBw4XNNJujfHz7+mNOnT5ORkeFwPbZWrVoOq4pvuukmn9uyJN4tLy+Pxo0bU1BQ4FGXMVS6Fzn/RuXn5+sfEfFeubnQvLnjgikgA4g49/vdQMsLPh9+0UsUV6vGvVFRfL9lC23atHG4606TJk1cmV6k0tavX8+4ceNIT083OooDXdO9SHZ2Nm3btlXhindLSir10xEX/P70uV+rAQ1Le7DJxP916EDDb74hJCTEqfFEXM0TF1GBSvcSutGB+ITNmy8Z5V7od+Chc7+fROml63/mDG1PnQIVrnghTy1dDecuoj264hPy8i77pcNAL2AN8DDwypVe59gxp8YScRdP3YWi0r2IRrrizQ4dOsTHH39Mek5OqV//BbAA6cA04G1si6suSydCiZfy1JGuppcvopGueAur1cq2bdsc7rpz9OhR4uLimNyiBWd37aLa6dMOz4kDDgDNgJPAxHOffwCIvvgbBAZCx44u/lOIOF9xcTG7d+/mhhtuMDrKJbR6+QKnT58mJCSEvLw8atasaXQcEQfnt+6cL9iUlBRq165tv+uOxWL54wbtl1m9fLlR7XvAiIs/GRAAe/a4/UxmkcrKysriz3/+M9u3bzc6yiU00r3Azp07adasmQpXPMLx48dZs2aNvWQzMjJo06YNFouFBx54gNdff53GjRuX/uT69W1nKS9Z4nD0Y1l/wraaTJj69lXhilfy1KllUOk60NSyGGnPnj32qeLVq1fz888/Ex0djcVi4ZlnnqFr167l27ozbZrtLOWyHAF5kVMmEwWjRqHKFW/kqYuoQKXrwJN/OhLfcvbsWX788UeHki0uLrYfPvHQQw/RuXNn/P39K/5NoqJsNy9ISChX8VqDgviuVy8eHjmS999/X8ehitfJzs6mV69eRscolUr3AllZWdxyyy1GxxAfdPLkSVJTU+0Fu27dOho2bIjZbOb222/nb3/7G9dff73zj6s7f9OCK91l6DyTCQIDMU2fzl1jxvDR8uUMHTqUhx56iBdeeIHq1fXPhXiH7OxsxlTyTlmuooVUF4iKimLWrFnExsYaHUW83Pm77pwv2a1bt9rvumOxWIiLi7Pdds9d0tNt99NdutRWrudv5we2VcpWK/Tta5uSvuAmB7/++ivDhg2jqKiI+fPnX/4asoiHsFqthIaG8ssvvxDmgVveVLrnWK1WQkJC2LNnj0e+UeK5rFYrO3bscNi68+uvvxIXF2cv2aioKAIDA42OCocP246I3LLFdvBFWJhtW9CIEZddNFVSUkJiYiKzZ8/mvffe44477nBrZJHy2L9/PxERERw6dMjoKKVS6Z6zb98+IiMjPfaNEs9x+vRpNmzY4DCSDQwMdNi644t33Vm5ciVDhgxh2LBh/O1vf9N0s3ik7777jpdffpnly5cbHaVU+ltzjhZRyeXk5eWxbt06e8Gmp6dz/fXXY7FYGDhwIDNnzqRZs2ZGx3S5Hj16sGHDBoYNG0avXr2YP3++7jYkHsfT/y1X6Z6j4x/lvH379jmsKs7JySEqKgqz2czUqVOJjY0lNDTU6JiGqFevHkuXLuWVV14hMjKSuXPn0rdvX6NjidipdL2E9uhWTSUlJWzdutWhZAsLC+33jh02bBhdunShRo0aRkf1GH5+fkybNo1u3boxePBgBg8ezMsvv1y57U0iTpKVlUW/fv2MjnFZuqZ7Tu/evXnyySe5/fbbjY4iLlRYWEhaWpq9YNeuXUu9evUcrse2bt3a+Vt3fNSRI0d48MEHOX78OAsWLKgS0+zi2Ro3bszatWs99v+LKt1zGjVqxLp16zz2jZKKOXLkiH3BU0pKCps2baJDhw4OW3caNGhgdEyvVlJSwvTp05kxYwbvvvsTh/RwAAAWGUlEQVQud999t9GRpIrKy8ujcePG5Ofne+xCRpUu3vFGydVZrVZ27tzpsKr4wIEDxMbG2k96io6OplatWkZH9UkpKSkMHjyY+Ph4EhMTNd0sbpeamsqYMWPIyMgwOspl6ZousG3bNtq2bavC9TLFxcVs2rTJ4Xps9erV6datG2azmXHjxtGxY0eqVatmdNQqwWw2s2HDBoYPH063bt1YuHAhzZs3NzqWVCGevogKVLqAFlF5i4KCAoetO6mpqbRs2RKLxUL//v2ZMWMGzZo10/VYA11zzTV8/vnn/POf/yQ6Opq3336bP//5z0bHkirCk290cJ5KF+/46agq2r9/v8NU8fbt24mIiMBsNjN58mRiY2N1epgH8vPzIyEhAYvFwv3338+KFSt45ZVXtAJcXC47O5shQ4YYHeOKVLp4xxvl60pKSsjKynI4SjE/P9++def1118nPDxc9zr2IjExMWRmZvLQQw9hsVhYuHAhLVu2NDqW+DBvGEBpIRXQrl07Fi9eTIcOHYyOUmUUFRWRnp5uL9iUlBTq1q3rsHWnbdu2mir2AVarlZkzZ5KYmMhbb71F//79jY4kPqi4uJjg4GDy8vI8+ofzKl+63vJGebujR4+yZs0a+3Txxo0bad++vb1gzWYz1113ndExxYXWr1/P/fffT79+/Xj11Vf1902cKjs7m7vvvpsdO3YYHeWKqvz0ck5ODk2bNtU/AE5ktVr5+eefHa7H7t27l5iYGCwWCy+++CJdu3aldu3aRkcVN+ratSuZmZmMHDkSs9lMcnIyrVq1MjqW+AhvWEQFKl2vuAbg6c6cOcPmzZsdtu4A9q07jz76KJ06ddJdaYSwsDA++eQT/vWvfxETE8Mbb7zBfffdZ3Qs8QHe8m95lf9XUDc6KL/ff/+d9evX2wt2/fr1NG3aFIvFwt13380rr7xCixYtdD1WSmUymZgwYQKxsbH21c3Tp08nICDA6GjixbKzs+nRo4fRMa6qypduVlYWPXv2NDqGRzt48KDDVHFWVhZdunTBYrHw+OOPExcXR926dY2OKV4mKiqKjIwMRo0aRVxcHMnJydxwww1GxxIvlZWVxejRo42OcVVVfiFVdHQ0M2fOJC4uzugoHsFqtZKdnW1fVbx69Wp+++03+9Ydi8VCRESERiXiNFarlTfeeIMXXniB2bNnM2jQIKMjiZexWq2Ehoaye/dujx8AVOnS9aY3ylVOnTpFRkaGw00BQkJCHFYVt2/fXkdkistlZmYSHx/PbbfdxmuvvaYf7KTM9u/fT3h4OL/++qvRUa6qSk8vHzhwgMDAwCpVuMeOHWPt2rX2qeLMzEzatm2LxWJhyJAhvPHGGzRu3NjomFIFhYeHk5GRwSOPPEJMTAzJycm0adPG6FjiBbxpbU6VLl1veqMqwmq1smfPHodVxbt37yY6OhqLxcKzzz5LTEwMwcHBRkcVASA0NJQFCxbw1ltvYTabmTVrFoMHDzY6lng4b1m5DFW8dH3tRgdnz55ly5YtDiV75swZ+1TxX/7yF26++Wbdck08mslkYvTo0cTExBAfH8+KFSuYOXMmgYGBRkcTD+Ute3ShipeuN/10VJoTJ06QmppqL9h169bRqFEjLBYLffr04eWXX6ZVq1bauiNeqXPnzqSnp/Poo4/ap5vbtm1rdCzxQOdPo/IGVb50veWNAvj111/t5xSvXr2aH3/8kZtvvhmLxcK4ceP48MMPufbaa42OKeI0ISEhfPTRR7zzzjtYLBZmzpypm5PIJbxp1rJKr15u3Lgxa9as8cgbbVutVrZv3+6wP/bw4cPExcXZVxVHRUVpyk2qjE2bNhEfH0+3bt2YNWsWQUFBRkcSD5Cfn0/Dhg0pKCjwil0WVbZ0Pe2NOn36NBs2bHC4tV1QUJDD/tibbrrJI7KKGKWgoIAxY8awadMmkpOTveY6nrhOWloajz76KJmZmUZHKZOqNb2cmwtJSbB5M2d37ya5Zk38pk+Hhx6CevXcGiUvL89h6056ejqtW7fGbDYzaNAgZs2aRdOmTd2aScTTBQcH88EHHzB37ly6d+/OjBkzePDBB42OJQbypkVUUFVGumlpkJgIy5bZPi4q+uNrgYFgtUKfPjBtGkRFuSTC3r17HVYV79q1i8jISPsoNiYmhtDQUJd8bxFftGXLFuLj44mJiWH27NnUqlXL6EhigKeffprAwECeffZZo6OUie+X7pw5kJAAhYW2cr0ck8lWwNOnw5gxlfqWZ8+eZevWrQ5TxYWFhQ43aO/SpYu27ohU0u+//87YsWPJyMggOTmZm266yehI4mYDBgxg8ODBDBw40OgoZeLbpXu+cE+eLPtzgoLKXbyFhYX2rTspKSmsWbOGBg0aOJTsDTfcoK07Ii5gtVpJSkriySef5P/+7/8YMWKE0ZHEjdq3b8+iRYvo0KGD0VHKxHdLNy0Neva8pHCHA98BR4BgIBJIBLpc+KCgIFi5EiIjS33pw4cPO2zd2bx5Mx07drSvKjabzdSvX98FfygRuZytW7cycOBAoqKieP3116ldu7bRkcTFiouLCQ4O5vjx415zVrfvlu6AAbBkySVTyj2BRkAo8D2wHWgG/HLhg0wm6N8fPv4Yq9VKTk6Ow9adQ4cOERsba19ZHB0dre0LIh7gxIkTPPbYY6xbt86rRj9SMdnZ2dx1113k5OQYHaXMfLN0c3OheXPHBVOlyAQiAD+gCLjwCuuZ6tV5+PbbWZqWRo0aNRymijt06EC1atVcl19EKuX9998nISGBf/zjH4wcOVKXdnzUkiVL+Pe//80XX3xhdJQy880tQ0lJV/zybOAn4H/nPp6MY+EClFitTAgJ4cW0NJo1a+b0iCLiOsOHDycqKor4+HiWL1/Om2++qelmH+SNR/n65kkLmzdfcZS7GJiDbWq5CWAu5TE1zp6lS/XqKlwRL3XjjTeSmppKQEAAERERbN682ehI4mTetkcXfLV08/Ku+OUVQCGwBDgA3MdF13TPO3bMycFExJ2CgoJ49913efbZZ+nduzdvv/02vnhFrarSSNdTXOaQiULg7LnfBwB3ALWBM8Cu0p4QFuaCcCLibkOHDuWHH35g9uzZPPDAA+Tn5xsdSSrJarWqdD1Gp05QyvLx9UBT4H5gDLZFVPlAPSD84gcHBkLHjq7NKSJu065dO9avX09wcDCRkZFs3LjR6EhSCQcPHiQgIIC6desaHaVcfLN0L7M5vhHQBvgW+DdwDBiIbevQJWNjq/WyryMi3ikwMJC3336bF198kdtuu405c+ZoutlLeeMoF3y1dOvXt52lfNE2gTbYruceBU4D+4Fk4JKdfCYT9O3r9psgiIh7DB48mJSUFN566y3uv/9+TTd7IW+6h+6FfLN0wXbzgoreazYw0PZ8EfFZbdq0Yd26ddStW5fw8HCvuTWc2GRnZ3vdymXw5dKNirKdoVzek6LOn718mSMgRcR3BAQEMGfOHF5++WVuv/12Xn/9dU03ewlvHen65olUFzLgLkMi4n1ycnKIj4/n+uuv591339WtNj1ckyZNSElJoXnz5kZHKRffHemeN2aM7eYF/fvbVjRfPOUcGGj7fP/+tsepcEWqpBtuuMF+h7Dw8HDS09ONjiSXUVBQwLFjx2jatKnRUcrN90e6Fzp82HZE5JYttoMvwsJs24JGjNCiKRGxW7x4MWPHjuWZZ55h/PjxOrvZw6SlpfHII4+wYcMGo6OUW9UqXRGRMtq5cyeDBg2iWbNmzJ07lzp16hgdSc754IMPWLZsGR999JHRUcrN96eXRUQq4PrrryclJYUmTZoQHh5Oamqq0ZHkHG/dowsqXRGRy6pZsyazZs1i+vTp3HXXXcycOVOrmz2AN97o4DyVrojIVQwYMID169fz0Ucf0b9/f3777TejI1VpGumKiPi4li1bsnr1alq2bEl4eDjr1q0zOlKVVFxczM8//0zr1q2NjlIhKl0RkTKqUaMGr732GjNnzqRfv37MmDFD081utmvXLho3bkxAKTe18QYqXRGRcrrnnntITU1l0aJF9OvXj6NHjxodqcrw5qllUOmKiFRIixYtWLVqFW3btiU8PJw1a9YYHalK8NbjH89T6YqIVFCNGjWYPn06s2fPpn///rz66quUlJQYHcuneeuNDs5T6YqIVNLdd99NWloan376KXfddRdHjhwxOpLP0khXRERo1qwZq1atokOHDoSHh7N69WqjI/kcq9Wqa7oiImLj7+/Pq6++ypw5c7j33nv5xz/+oelmJzp06BA1a9bkmmuuMTpKhal0RUSc7M477yQ9PZ0vv/ySO++8k8OHDxsdySd4+9QyqHRFRFyiadOmLF++nM6dO9OlSxdWrVpldCSv5+2LqEClKyLiMv7+/iQmJvLOO+8QHx/Pyy+/rOnmStBIV0RErqpPnz5kZGTwzTffcMcdd5Cbm2t0JK+kka6IiJRJ48aN+f7774mKiiI8PJwVK1YYHcnrePvKZdBN7EVE3O6///0vw4cPZ8yYMfz1r3+lWrVqRkfyeAUFBVx33XUUFBTg5+e940XvTS4i4qX+9Kc/kZGRwffff8/tt9/OoUOHjI7k8bZt20abNm28unBBpSsiYohGjRrx3XffERcXR3h4ON9//73RkTyaL0wtg0pXRMQw1atX56WXXmLevHkMHTqU559/nrNnzxodyyP5wsplUOmKiBju1ltvJSMjgx9++IHbbruNgwcPGh3J4/jCymVQ6YqIeISGDRvy7bff0r17dyIiIvj222+NjuRRfGWkq9XLIiIe5vvvv2fYsGGMHDmS559/nurVqxsdyVDFxcUEBwdz/PhxAgICjI5TKRrpioh4mFtuuYWMjAzWrl1L7969OXDggNGRDPXzzz/TuHFjry9cUOmKiHik6667jm+++YZbb72ViIgIvvnmG6MjGcZXppZBpSsi4rGqVavGs88+y/z58/nLX/7C008/zZkzZ4yO5Xa+sogKVLoiIh6vZ8+eZGZmkp6eTq9evdi3b5/RkdxKI10REXGr+vXr8/XXX9OnTx8iIyNZtmyZ0ZHcxlcOxgCtXhYR8TqrVq1iyJAhDBkyhL/97W/4+/sbHcllrFYrYWFh7Ny5k2uuucboOJWmka6IiJfp3r07mZmZbNq0iZ49e7J3716jI7nMoUOH8Pf394nCBZWuiIhXqlevHl999RX9+vUjMjKSL7/80uhILuFLi6hApSsi4rX8/PyYOnUqH3/8MWPHjmXKlCkUFxcbHcupfGkRFah0RUS8nsViITMzk59++onu3bvzyy+/GB3JaXxpERWodEVEfMK1117LF198wYABA4iOjubzzz83OpJT+Nr0slYvi4j4mDVr1jB48GDuu+8+EhMTqVGjhtGRKqxp06asWrWKli1bGh3FKTTSFRHxMXFxcWRmZrJjxw66devG7t27jY5UIQUFBRw9epTmzZsbHcVpVLoiIj7ommuu4bPPPmPQoEFER0ezZMkSoyOV2/bt22nTpg1+fr5TVb7zJxEREQcmk4lJkybx+eefM3HiRCZOnMjp06eNjlVmvrZyGVS6IiI+LyYmhg0bNrB7927MZjO7du0yOlKZ+NoiKlDpiohUCWFhYXz66acMGTKEmJgYPv74Y6MjXZVGuiIi4rVMJhMTJ07kq6++YsqUKYwfP55Tp04ZHeuyfG2PLqh0RUSqnKioKDIzMzlw4ABxcXHs3LnT6EiXOHPmDLt27aJNmzZGR3Eqla6ISBVUp04dFi9ezIgRI4iNjWXRokVGR3Kwa9cuGjZsSGBgoNFRnEqlKyJSRZlMJsaPH8+yZct46qmnGDt2LEVFRUbHAnxzERWodEVEqryIiAgyMzM5fPgwsbGx7Nixw+hIPrmIClS6IiIChIaGkpyczMMPP0xcXBwLFiwwNI8vLqICla6IiJxjMpkYO3Ys33zzDc8++yyjR4+msLDQkCyaXhYRkSohPDycjIwMjh8/TkxMDNu2bXPr97darZpeFhGRqiMkJIT58+czZswYLBYLH374odu+96+//oq/vz/XXnut276nu6h0RUSkVCaTidGjR/Ptt9/y4osv8vDDD7tlutlXr+eCSldERK6ic+fOZGRkcOLECaKjo8nOznbp9/PVqWVQ6YqISBkEBwfz4YcfMmHCBLp168YHH3zgsu/lq4uoQKUrIiJlZDKZePjhh/nf//7Hyy+/zMiRIzl58qTTv49GuiIiIud06tSJ9PR0iouLiY6O5qeffnLq6+uaroiIyAVq167NvHnzmDRpEj169CApKckpr/v7779z5MgRmjdv7pTX8zQqXRERqRCTycTIkSNZvnw5r7zyCiNGjODEiROVes1t27bRunVrqlWr5qSUnkWlKyIildKhQwfS09MB220Df/zxxwq/li8vogKVroiIOEGtWrVISkriySefpFevXsydOxer1Vru1/HlRVSg0hUREScaMWIEK1euZMaMGTz44IP8/vvv5Xq+Ly+iApWuiIg42Y033khqair+/v5ERkayZcuWyz84NxdefRWGDoW772b4d9/Rfd06OHzYfYHdyGStyPhfRESkDD744AMmTZrE3//+d0aNGoXJZLJ9IS0NEhNh2TLbx0VF9udYAwIwAfTpA9OmQVSU23O7ikpXRERcKisri/j4eDp16sSbb75J8H/+AwkJUFgIV6ogkwkCA2H6dBgzxn2BXUilKyIiLnfy5Ekef/xxGn3+Oc/l51PtgpHtVQUF+UzxqnRFRMQ90tI4Y7FQ/fRp+6dmAnOBrUAJ8DzwQmnPDQqClSshMtINQV1HC6lERMQ9EhOpXlzs8KkMoC7Q9GrPLSy0XQP2cipdERFxvdxc26KpiyZXPwBWAJ2v9nyrFZYu9fpVzSpdERFxPWeczWwyOed1DKTSFRER19u82WFbUIUUFsKV9vx6AZWuiIi4Xl6ec17n2DHnvI5BVLoiIuJ6oaHOeZ2wMOe8jkFUuiIi4nqdOkFAwCWffhcYAWSe+3jJuY+XlPYagYHQsaNr8rmJ9umKiIjr5eZC8+aXXNcdAbxfysNL3a8bEAB79kC9eq5I6BYa6YqIiOvVr287S/n82cvnJAHWUv574eLnm0zQt69XFy5opCsiIu6SlgY9e8LJk+V/rk6kEhERKYeoKNsZykFB5Xve+bOXvbxwAaobHUBERKqQ8zct0F2GRERE3CQ93XaW8tKltnItLPzja4GBtjLu29d2P10fGOGep9IVERHjHD5sO9pxyxbbwRdhYbZtQSNGeP2iqdKodEVERNxEC6lERETcRKUrIiLiJipdERERN1HpioiIuIlKV0RExE1UuiIiIm6i0hUREXETla6IiIibqHRFRETcRKUrIiLiJipdERERN1HpioiIuIlKV0RExE1UuiIiIm6i0hUREXETla6IiIibqHRFRETcRKUrIiLiJipdERERN1HpioiIuIlKV0RExE1UuiIiIm6i0hUREXETla6IiIibqHRFRETcRKUrIiLiJipdERERN1HpioiIuIlKV0RExE1UuiIiIm6i0hUREXETla6IiIibqHRFRETc5P8DYSkE9IazfH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IBM - limited to 5, 14 or 20 qubits (these are the only current physical architectures)\n",
    "ibm_env = gym.make('quantcircuit-v0')\n",
    "ibm_env.var_init(5,connectivity='ibm')\n",
    "ibm_env.plot_connectivity_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z8/Xoo4/q3LlzstvtCgkJMR0JgIdj6xsoR40bN9by5cvVq1cvRUZGat26daYjAfBwrKiBCrJq1So9/vjjevbZZ/Xqq6+qShX+LgZw8yhqoAJ9++23Sk5OVq1atfTxxx+rYcOGpiMB8DAUNVDBiouLNX78eKWkpGjmzJmKiYkp+8H5+dLUqdL27dKpU1K9elL79tKoUVKjRpWWGYD7oKiBSrJo0SI9+eSTevnll/XCCy/Iz8/v529u3ixNmCBduLyrqOjn7wUESA6HFBsrjRsnRUVVbnAARlHUQCU6ePCghg0bptDQUE2dOlV33HGHNHmy9OKLUmGhs5DL4ufnLO2JE6Wnn6680ACM4nQLUInuuusurV+/Xs2bN1dERIQOjRvnLOmCgmuXtOT8fkGB8/GTJ1dOYADGsaIGDFk9YYJiXn1VgRd97UlJGZIOS6opqbOkv0i67/InBwZK6elSZGSlZAVgDitqwJC+mzcr4OL3qSV9KOkOSTZJdSUtkzRAUtHlTy4sdL6nDcDrsaIGTMjPl8LCLj00JilLUsT5jw9KanHR1ztd/hr+/lJuLqfBAS/HihowYerUq3454qKPz57/t6qkqw4i9fMr83UAeA+KGjBh+/YrVtMXOyNp1PmPf6MyirqwUMrJKf9sANwKRQ2YcOpUmd86LulBSRsk/VLSn6/1OidPlmssAO6HogZMqFfvql8+JKm7pC2Sxkl6X5LfVR95XlBQeScD4GYoasCE9u2dh8EuEyNpt6Tmkgok/fr8f5lXeYmfqlbVdkk//vhjBQYFYBqnvgETyjj1Xdbq+SNJIy/72rlq1ZQcE6OVW7eqZ8+eio+P16BBg9SkSZMKCAzAFFbUgAmNGztnd192HbWjjP9GXv58Pz9VGzxYc9PTlZubK5vNplWrVik8PFzdunXTX//6V+3evbvifw8AFY4VNWDK5s1Sr17OsaA3q4zJZD/99JPWrVuntLQ0paWlqV69ekpISFB8fLyio6O5JzbggShqwKQLN+S4mbIODLyhG3OUlpZqy5YtSk1NVVpamr7//nsNHjxY8fHx6t27t/yv8h45APdDUQOmVdLds/bs2eNaaefk5Khfv35KSEhQXFycgjg9DrgtihpwB1u2OGd3L13qLOTCQte3CiX5+/vLLy7OeT/qcrgRR35+vhYvXqzU1FR98sknioqKcm2RN2/e/LZfH0D5oagBd3L8uHMsaE6Oc5hJUJDeXLVK3f/1L3UeOLBCfuSPP/6oVatWKTU1VYsXL1bz5s0VHx+vhIQEtW/fXn5+17ySG0AFo6gBN/faa6/phx9+0JtvvlnhP+vcuXPasGGD633tkpISV2n36NFD1apVq/AMAC5FUQNuLicnR4MGDdKBAwcqdXXrcDi0Y8cOpaWlKTU1VQcOHNDDDz+s+Ph49e/fX7Vr1660LIAvo6gBN+dwONS6dWvNmjVLnTpdcbPLSnP48GEtXLhQaWlp2rRpkx544AHXkJXg4GBjuQBvR1EDHuCVV15R1apV9cYbb5iOIkn697//rWXLliktLU3Lly9X27ZtXYfRwsPDTccDvApFDXiAzMxMPfHEE9q5c6fpKFf46aef9Mknn7gu/apTp46rtDt37syQFeA2UdSAB3A4HGrevLlWrFihtm3bmo5TptLSUmVlZbkOo3333XcaNGiQ4uPj1adPH4asALeAogY8xPPPP69GjRpp/PjxpqPcsL1797pW2tu2bXMNWXn44YcZsgLcIIoa8BDp6el64YUXlJ2dbTrKLTl+/LhryMq6desUFRWl+Ph4xcfHKywszHQ8wG1R1ICHKCkpUWhoqDZu3KiWLVuajnNbLgxZSUtL0+LFi9WsWTPX9dodOnRgyApwEYoa8CBjxoxReHi4xo4dazpKubkwZOXC9dolJSUaPHiwa8hK9erVTUcEjKKoAQ+yYsUKvf7669qwYYPpKBXC4XDoyy+/dJX2/v37FRcX5xqyUqdOHdMRgUpHUQMe5OzZswoODtaOHTsUGhpqOk6F++abb1xDVjZu3KgePXooPj5egwcPZsgKfAZFDXiYxx9/XJ07d9azzz5rOkqlOnXq1CVDVu655x7X9dr33HOP6XhAhaGoAQ+Tmpqqd955R2vWrDEdxZizZ8/qk08+UWpqqhYuXKjatWu7TpB36dKFISvwKhQ14GEKCgoUEhKiffv2qWHDhqbjGHdhyMqF67WPHz/uGrLSt29fhqzA41HUgAdKSkpSbGysRo8ebTqK29m3b5/rMNq2bdvUt29f15CV+vXrm44H3DSKGvBAKSkpmjFjhhYvXmw6ils7fvy4lixZotTUVK1du1aRkZGuLfK77rrLdDzghlDUgAc6ffq0mjVrpm+++UZ169Y1HccjFBQUXDJkJTQ01DVk5f7772fICtwWRQ14qIEDB+rRRx+VzWYzHcXjlJSUXDJkpbi42LXSfuCBBxiyArdCUQMe6qOPPtKSJUs0d+5c01E8msPh0FdffeW649fevXtdQ1YGDBjAkBUYR1EDHurEiRNq1aqVjhw5osDAQNNxvMa3337rGrKyYcMGde/e3TVkJSQkxHQ8+CCKGvBgffr00XPPPaeEhATTUbzSqVOntHz5cqWlpWnZsmUKDw+/ZMgK72ujMlDUgAebNGmSNm7cqOnTp5uO4vXOnj2r9PR01xZ5rVq1XIfROnfurKpVq5qOCC9FUQMeLC8vT/fdd5+OHj2qGjVqmI7jMxwOxyVDVo4dO6ZBgwYpISFBffr0UUBAgOmI8CIUNeDhunXrpt/97ncaMGCA6Sg+a//+/a4T5F988YX69u2r+Ph4Pfzww2rQoIHpePBwFDXg4d58803t2rVL77//vukokPOQ3+LFi5WWlqY1a9YoIiLCdelXixYtTMeDB6KoAQ934MABde7cWUeOHOF9UjdTUFCg1atXKy0tTYsWLVJISIjrfe2OHTtyGA03hKIGvECnTp301ltvqWfPnqajoAwlJSXauHGja4v8p59+cq20e/bsyZAVlImiBrzA//zP/yg/P19///vfTUfBDXA4HNq5c6frBPmePXsUGxurhIQEhqzgChQ14AV27typfv36KTc3l3sxe6C8vDzXkJWMjAx169ZNCQkJDFmBJIoa8Bpt2rTRtGnTFB0dbToKbsPp06e1fPlypaamatmyZWrdurVryEqbNm14X9sHUdSAlxg/fryKi4v15z//2XQUlJOzZ8/q008/dW2RBwQEuA6jdenShcODPoKiBrxEdna2hg8frt27d7Pq8kIOh0PZ2dmuw2jHjh3TwIEDlZCQoL59+zJkxYtR1ICXcDgcatmypdLS0tS+fXvTcVDB9u/fr4ULFyo1NVVbt25Vnz59FB8fr4EDBzJkxctQ1IAXGTt2rOrUqaPXXnvNdBRUohMnTmjJkiWuISsdO3Z0XfrVsmVL0/FwmyhqwItkZGToV7/6lXJyckxHgSGFhYWuISsLFy5UcHCw6zBap06deFvEA1HUgBcpLS1V06ZNlZ6ertatW5uOA8NKSkq0adMm12G0wsLCS4ascCMXz0BRA17mmWeeUVhYmF5++WXTUeBGHA6Hvv76a1dp7969WwMGDHANWalbt67piCgDRQ14mTVr1mjcuHHKzMw0HQVuLC8vT4sWLVJqaqoyMjIUExPjGrISGhpqOh4uQlEDXqa4uFghISHKzs5W8+bNTceBBzh9+rRWrFjhGrLyi1/8wrVF3rZtW97XNoyiBrzQ6NGj1aFDBz3//POmo8DDFBcXKz093XW9tr+/v2vISteuXRmyYgBFDXihxYsX669//avS09NNR4EHczgc2rp1q6u0jxw5okGDBik+Pl79+vVjyEoloagBL1RUVKTg4GDt2rVLTZo0MR0HXuLAgQOuIStZWVmXDFlp2LCh6Xhei6IGvJTNZtODDz6oMWPGmI4CL/Tdd9+5hqysXr1a999/v+t97VatWpmO51UoasBLzZkzRx988IFWrFhhOgq8XGFhodasWaPU1FQtWrRIjRs3dg1ZiYiI4DDabaKoAS915swZhYaG6tChQwoKCjIdBz6ipKREn3/+uVJTU5WamqqCggLXSrtXr14MWbkFFDXgxRISEjR06FCNGDHCdBT4oAtDVtLS0pSWlqavv/7aNWQlNjaWISs3iKIGvNj06dM1b948paammo4C6MiRI64hK5999pliYmIUHx+vwYMHq2nTpqbjuS2KGvBiJ0+eVFhYmPLy8lS7dm3TcQCXH374wTVkZenSpbr77rtdW+T33nsv72tfhKIGvFz//v315JNPKikpyXQU4KqKi4v16aefuq7XrlGjhmvISkxMjM8PWaGoAS/3/vvva926dUpJSTEdBbguh8OhL774wnXzkG+//faSISuBgYHl+wPz86WpU6Xt26VTp6R69aT27aVRo6RGjcr3Z90iihrwcseOHVN4eLiOHj0qf39/03GAm3Lw4EHXYbQtW7aod+/eriErjW6nSDdvliZMkJYtc35eVPTz9wICJIdDio2Vxo2ToqJu75e4TRQ14AN69uypl156SQMHDjQdBbhl33//vWvIyqpVq9ShQwfX9do3NWRl8mTpxRelwkJnIZfFz89Z2hMnSk8/ffu/wC2iqAEf8Pbbb2vbtm2aMmWK6ShAuSgqKrpkyErDhg0vGbJSpUqVqz/xQkkXFNz4DwsMNFrWFDXgAw4fPqyOHTvqyJEjql69uuk4QLkqLS29ZMjKjz/+qMGDBys+Pl4PPvjgz0NWNm+WevW6pKSLJL0kaZakHyR1kvT/S+p8+Q8JDJTS06XIyIr/hS5DUQM+Ijo6WhMmTFCfPn1MRwEq1IUhK6mpqfr666/Vv39/JSQkaMjHH6vG0qWXbHf/StI/Jd13/r9ZkmpL2i/pktuM+PlJjzwizZtXeb/IhR9NUQO+4U9/+pNyc3M1adIk01GASnNhyMons2drypo1uvg4Zb6kZpJKJB2R1FjSCEkfS/qDpNcufzF/fyk3t9JPg5exiQ/A2wwZMkQLFixQaWmp6ShApQkJCdGYMWNkf+gh1bzsqocvJRVLai5nSUvShY3tL672Yn5+zku5KhlFDfiI1q1bq2HDhtq4caPpKEDl275dfhdfgiXp2Pl/L57ZV+v8v0ev9hqFhVJOTvlnuw6KGvAhQ4cO1fz5803HACrfqVNXfKnJ+X/PXPS1Cx8Hl/U6J0+WX6YbRFEDPmTIkCGaN2+eOJoCX1Nap84VX2srqbqkXP28ut58/t8OZb2QgVvGUtSAD2nXrp2qV6+urVu3mo4CVLjS0lJ99tlnevbZZ/XHtDQVXXajjyaSRkoqldRHUrKkFDm3wv+/q71gQIDUrl2FZr4aihrwIX5+fhoyZAjb3/BaDodD27dv1yuvvKIWLVroqaeeUtOmTfXEunXyr1nzise/LekZOVfUqZK6SFop6arnuh0OaeTIigtfBi7PAnzM559/rpEjR2rnzp2mowDl5sCBA0pJSZHdbtcPP/wgm80my7LUrl27n2+ZOWSIlJp67bGhZeE6agCVpbS0VGFhYVq5cqXatGljOg5wy44dO6bZs2fLbrdr7969GjZsmGw2m2JiYq4+QvQqk8lumMHJZGx9Az6mSpUqeuSRRzTPwMoAuF2nT5/WtGnT1L9/f4WHhyszM1O///3vlZeXp3fffVfdu3cve853VJRzZvfN3irzwqxvAyUtsaIGfFJ6erpeeOEFZWdnm44CXFdRUZGWLl2qlJQUrVy5Ur169ZJlWRo0aNCt3Z+au2cBcHclJSUKCQnRpk2b1LJlS9NxgCuUlJRo3bp1stvtSk1N1f333y/LsjRkyBDVr1//9n/Ali3O+1EvXeos5MLCn7934X7UcXHO+1EbWklfQFEDPmrMmDEKDw/X2LFjTUcBJDlPbG/evFl2u12zZs1S06ZNZVmWhg8frqZNm1bMDz1+3DkWNCfHOcwkKMh5CdbIkZU+07ssFDXgo5YvX64//vGPysjIMB0FPm7nzp2y2+1KSUlRlSpVZFmWbDabwsPDTUdzCxQ14KPOnj2r4OBg7dixQ6GhoabjwMccPnxYM2fOlN1uV35+vpKTk2VZljp16vTz5VSQRFEDPm3EiBHq2rWrnnnmGdNR4AO+++47zZ2VhdSbAAALLElEQVQ7V3a7XTt27NCQIUNkWZYeeOABVa1a1XQ8t0VRAz5swYIF+sc//qE1a9aYjgIvdebMGS1cuFB2u13r169XbGysLMtS//79VfMqk8JwJYoa8GEFBQUKCQnRvn371LBhQ9Nx4CXOnj2rlStXym63a+nSpYqJiZFlWYqPj1edq9wcA9dGUQM+LjExUXFxcRo9erTpKPBgpaWlWr9+vex2u+bNm6c2bdrIZrMpKSlJjdzk9LSnqmY6AACzhg4dqhkzZlDUuGkOh0NffPGF7Ha7Zs6cqfr168uyLGVlZSksLMx0PK/BihrwcadPn1azZs30zTffqG7duqbjwAPs3bvXdQOMoqIi1+VU9913n+loXokVNeDj6tatqx49emjJkiWy2Wym48BNHTlyRLNmzZLdbtehQ4c0fPhwffTRR+rcuTOXU1UwVtQANGXKFC1btkxz5swxHQVu5N///rfmz58vu92urKwsxcfHy7Is9e7dW9Wqsc6rLBQ1AJ04cUKtWrXSkSNHbu0mB/AahYWFWrJkiex2u9asWaO+ffvKsizFxcUpICDAdDyfxJ9EANSwYUNFRkZq5cqVSkhIMB0HlezcuXNas2aN7Ha7Fi5cqMjISFmWpSlTpuiOO+4wHc/nsaIGIEl69913tWnTJk2fPt10FFQCh8OhTZs2yW63a/bs2WrRooVsNpuGDRumkJAQ0/FwEYoagCQpLy9P9913n44ePaoaNWqYjoMKsmPHDtcNMPz9/fXoo48qOTlZd999t+loKANb3wAkSaGhobrnnnu0du1aDRgwwHQclKODBw+6boBx8uRJ2Ww2LViwQB06dODEtgegqAG4DBkyRPPnz6eovUB+fr7mzJmjlJQU7dq1S4mJiXr33XfVrVs3ValSxXQ83AS2vgG47N+/X127dlVeXh53M/JAP/zwg1JTU2W327Vx40Y9/PDDsixL/fr14+0MD8aKGoBLy5YtFRoaqs8++0w9e/Y0HQc34KefftLy5ctlt9u1fPly9ezZU48//rjmzp2rWrVqmY6HckBRA7jE0KFDNX/+fIrajZWUlCg9PV12u10LFixQu3btZFmWJk2apAYNGpiOh3LG1jeAS3z11Vfq37+/Dh06xHuZbsThcCgrK8t1A4zg4GBZlqXhw4frzjvvNB0PFYgVNYBLtG3bVrVr19aWLVsUHR1tOo7P27Vrl+sGGA6HQ5Zlae3atbrnnntMR0MloagBXGHIkCGaN28eRW3It99+67qcKi8vT8nJyZoxY4YiIyO5nMoHsfUN4ApZWVlKTk7W7t27KYZK8v3332vevHmy2+3atm2bHnnkEVmWpV69enEC38dR1ACu4HA41KJFCy1atEjt2rUzHcdr/fjjj1q0aJHsdrvS09PVv39/WZal2NhY1axZ03Q8uAm2vgFcwc/Pz7X9TVGXr+LiYq1atUp2u12LFy9Wly5dZFmWPv74Y9WtW9d0PLghVtQAriojI0NPP/20tm/fbjqKxystLVVGRoZSUlI0Z84ctW7dWpZlKSkpSY0bNzYdD26OFTWAq+ratauOHz+uPXv26Be/+IXpOB7H4XBo+/btrhtg1KtXT5ZlKTMzUy1atDAdDx6EFTWAMj3zzDMKCwvTyy+/bDqKx9i/f7/rcqoff/xRlmXJZrPxFgJuGUUNoEyrV6/Wq6++qszMTNNR3NqxY8c0e/Zs2e127du3T8OGDZNlWeratSun5nHbKGoAZSouLlZwcLC2bt2q5s2bm47jVk6dOqUFCxbIbrdr8+bNGjRokCzLUp8+fVS9enXT8eBFKGoA1zRq1Cjdf//9ev75501HMa6oqEhLly6V3W7XqlWr1Lt3b1mWpYcffliBgYGm48FLUdQArmnRokWaOHGi0tPTTUcx4ty5c1q3bp3sdrvS0tLUsWNHWZalIUOGKCgoyHQ8+ACKGsA1FRUVKTg4WLt27VKTJk1Mx6kUDodDmZmZstvtmjVrlpo3by6bzabhw4crNDTUdDz4GC7PAnBN/v7+GjBggNLS0jRmzBjTcSrUV1995bqcqnr16rIsS+vXr+fyNBjFihrAdc2ZM0cffvihli9fbjpKucvNzXXdAOPEiRNKTk6WZVnq2LEjJ7bhFihqANd15swZhYaG6tChQ17xvuyJEyc0d+5c2e12ffXVVxo6dKgsy1KPHj24BzfcDkUN4IbEx8crMTFRI0aMMB3llpw5c0ZpaWmy2+3KyMhQbGysLMtS//79VaNGDdPxgDJR1ABuyP/93/9p/vz5Sk1NNR3lhp09e1YrVqyQ3W7XsmXL1L17d9lsNsXHx6t27dqm4wE3hKIGcENOnjypsLAw5eXluXXJlZaWav369bLb7Zo3b57atm0ry7KUmJiohg0bmo4H3DROfQO4IUFBQeratauWL1+uxMRE03Eu4XA4tHXrVtntds2cOVONGjWSzWZTdnY2E9Xg8ShqADfswj2q3aWo9+zZ47oBRnFxsSzL0sqVK9W2bVvT0YByw9Y3gBt27NgxhYeH6+jRo/L39zeSIS8vT7NmzVJKSooOHz7sugFGdHQ0l1PBK7GiBnDDmjRpovbt22v16tUaOHBgpf3ckydPav78+bLb7dq6dasSEhL0v//7v+rVq5eqVeN/Y/BurKgB3JS//e1vOvD553q7Y0dp+3bp1CmpXj2pfXtp1CipUaNy+TmFhYVavHix7Ha71q5dq379+smyLMXFxRlbzQMmUNQAbtzmzSr43e9UZcUK1fT3l19R0c/fCwiQHA4pNlYaN06Kirrplz937pxWr14tu92uRYsWKTo6WpZlKSEhQfXq1SvHXwTwHBQ1gBszebL04otSYaGzkMvi5+cs7YkTpaefvu7LOhwObdy4UXa7XXPmzFHLli1lWZaGDRvmMzcBAa6FN3cAXN+Fki4ouP5jHQ7n41580fl5GWW9Y8cO1w0wAgMDZVmWNm7cqJYtW5ZjcMDzsaIGcG2bN0u9el1S0n+TNEXSl5JKJf1B0mtXe25goJSeLkVGSpIOHjzoupzq9OnTstlssixL7dq148Q2UAZW1ACubcIE53b3RbIk1Zd0p6RD13puYaGKXntNH8bGym63a/fu3UpKStLkyZMVExPDDTCAG0BRAyhbfr60bNkV70lPP/9vgq5T1A6HtGSJvgwI0G9/+1v169dP1atXr6CwgHeiqAGUberU236Jmv7+mhQdLcXF3X4ewAex7wSgbNu3SxdfgnUL/IqKpJyccgoE+B6KGkDZTp0qn9c5ebJ8XgfwQRQ1gLKV15CRoKDyeR3AB1HUAMrWvr10lXGdH0gaKSn7/Oep5z9PvdprBARI7dpVTD7AB3AdNYCy5edLYWFXvE89UtK0qzz8qtdT+/tLubnlNgMc8DWsqAGUrXFj5+zuy4aRTJXkuMp/r13+fD8/52lvShq4ZayoAVzbVSaT3bDLJpMBuHmsqAFcW1SU8wYbgYE397zAQOfzKGngtjDwBMD1XbixRgXcPQvAtbH1DeDGbdninP29dKmzkC+eAX7hftRxcc77UbOSBsoFRQ3g5h0/7hwvmpPjHGYSFOS8BGvkSA6OAeWMogYAwI1xmAwAADdGUQMA4MYoagAA3BhFDQCAG6OoAQBwYxQ1AABujKIGAMCNUdQAALgxihoAADdGUQMA4MYoagAA3BhFDQCAG6OoAQBwYxQ1AABujKIGAMCNUdQAALgxihoAADdGUQMA4MYoagAA3BhFDQCAG6OoAQBwYxQ1AABujKIGAMCNUdQAALgxihoAADdGUQMA4MYoagAA3BhFDQCAG6OoAQBwYxQ1AABujKIGAMCN/T8Lx70RVMuAhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom\n",
    "custom_conn = np.array([[1,1,1,0],[1,1,1,0],[1,1,1,1],[0,0,1,1]])\n",
    "custom_env = gym.make('quantcircuit-v0')\n",
    "custom_env.var_init(4,connectivity='custom',custom_connectivity=custom_conn)\n",
    "custom_env.plot_connectivity_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generating goal curriculums\n",
    "An added feature of the environment is the ability to generate a curriculum of goals, that starts out with relatively 'easy' goal state or unitaries and gets progressively harder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_gates = 2\n",
    "curriculum, tracker = env.make_curriculum(num_gates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curriculum contains the goals generated using only single gates, and combinations of two different gates while tracker contains the number of each type of goal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(1+0j), 0j, 0j, 0j, 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), (0.7071+0j), 0j, 0j, 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), 0j, (0.7071+0j), 0j, 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), 0j, 0j, 0j, (0.7071+0j), 0j, 0j, 0j],\n",
       " [0j, (1+0j), 0j, 0j, 0j, 0j, 0j, 0j],\n",
       " [0j, 0j, (1+0j), 0j, 0j, 0j, 0j, 0j],\n",
       " [0j, 0j, 0j, 0j, (1+0j), 0j, 0j, 0j],\n",
       " [(0.5+0j), (0.5+0j), (0.5+0j), (0.5+0j), 0j, 0j, 0j, 0j],\n",
       " [(0.5+0j), (0.5+0j), 0j, 0j, (0.5+0j), (0.5+0j), 0j, 0j],\n",
       " [0j, 0j, (0.7071+0j), (0.7071+0j), 0j, 0j, 0j, 0j],\n",
       " [0j, 0j, 0j, 0j, (0.7071+0j), (0.7071+0j), 0j, 0j],\n",
       " [(0.7071+0j), (-0.7071+0j), 0j, 0j, 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), 0j, 0j, (0.7071+0j), 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), 0j, 0j, 0j, 0j, (0.7071+0j), 0j, 0j],\n",
       " [(0.5+0j), 0j, (0.5+0j), 0j, (0.5+0j), 0j, (0.5+0j), 0j],\n",
       " [0j, (0.7071+0j), 0j, (0.7071+0j), 0j, 0j, 0j, 0j],\n",
       " [0j, 0j, 0j, 0j, (0.7071+0j), 0j, (0.7071+0j), 0j],\n",
       " [(0.7071+0j), 0j, (-0.7071+0j), 0j, 0j, 0j, 0j, 0j],\n",
       " [(0.7071+0j), 0j, 0j, 0j, 0j, 0j, (0.7071+0j), 0j],\n",
       " [0j, (0.7071+0j), 0j, 0j, 0j, (0.7071+0j), 0j, 0j],\n",
       " [0j, 0j, (0.7071+0j), 0j, 0j, 0j, (0.7071+0j), 0j],\n",
       " [(0.7071+0j), 0j, 0j, 0j, (-0.7071+0j), 0j, 0j, 0j],\n",
       " [0j, 0j, 0j, (1+0j), 0j, 0j, 0j, 0j],\n",
       " [0j, 0j, 0j, 0j, 0j, (1+0j), 0j, 0j],\n",
       " [0j, 0j, 0j, 0j, 0j, 0j, (1+0j), 0j]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curriculum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 18]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracker"
   ]
  }
 ],
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