Lab2/lab2.ipynb

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    "# Lab 2: Quantum teleportation\n",
    "\n",
    "Welcome to this lab on quantum teleportation in which you'll solve a problem that Alice and Bob have been having. Relax, it's not a relationship problem! Alice possesses a qubit in an unknown state $\\lvert \\psi \\rangle$ and she wishes to transfer this quantum state to Bob. However, they are very far apart and lack any means to transfer quantum information directly, only classical information. Is it possible to achieve their goal?\n",
    "\n",
    "It turns out that if Alice and Bob share an entangled qubit pair, she can transfer her qubit state to Bob by sending two bits of classical information. This process is known as teleportation because, at the end, Bob will possess |ψ⟩, and Alice will no longer have it."
   ]
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    "## Background\n",
    "\n",
    "Quantum teleportation is a protocol that allows the transfer of quantum information from one qubit to another using entanglement and classical communication. It was proposed by Charles Bennett, Gilles Brassard, Claude Crépeau, Richard Jozsa, Asher Peres, and William Wootters in 1993. The process does not transmit the qubit itself but rather transfers the quantum state from the source qubit to the target qubit.\n",
    "\n",
    "The protocol requires three qubits:\n",
    "\n",
    "1. The qubit to be teleported (Alice's qubit)\n",
    "2. One half of an entangled pair of qubits (Alice's second qubit)\n",
    "3. The other half of the entangled pair (Bob's qubit)\n",
    "\n",
    "The protocol can be summarized in the following steps:\n",
    "\n",
    "1. Create an entangled pair of qubits (Bell pair) shared between Alice and Bob.\n",
    "2. Alice performs a Bell basis measurement on her two qubits.\n",
    "3. Alice sends the classical results of her measurement to Bob.\n",
    "4. Bob applies appropriate quantum gates based on Alice's measurement results to obtain the teleported state."
   ]
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    "## Implementation\n",
    "\n",
    "In order to transfer a quantum bit, Alice and Bob require the help of a third party who provides them with a pair of entangled qubits. Next, Alice carries out certain operations on her qubit and shares the results with Bob through a classical communication channel. Finally, Bob performs a series of operations on his end to successfully obtain Alice's qubit. Now, let's delve deeper into each of these steps."
   ]
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    "Our quantum circuit will consist of 3 qubits and 3 classical bits. The qubits will be named as follows:\n",
    "- $s$: The \"source\" qubit containing the state $\\lvert \\psi \\rangle$ which Alice wishes to transmit to Bob.\n",
    "- $a$: The qubit which will initially store Alice's half of the entangled Bell pair.\n",
    "- $b$: The qubit which will initially store Bob's half of the entangled Bell pair.\n",
    "\n",
    "The teleportation protocol itself requires 2 classical bits, and we include a third one to use for measuring Bob's final state. The classical bits will be named as follows:\n",
    "- $c0$: The classical bit that Alice uses to measure $a$.\n",
    "- $c1$: The classical bit that Alice uses to measure $s$.\n",
    "- $c2$: The classical bit that Bob uses to measure $b$."
   ]
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   "source": [
    "### Exercise 1\n",
    "\n",
    "Utilize two qubits to generate an entangled Bell pair state $\\frac{\\lvert 00 \\rangle + \\lvert 11 \\rangle}{\\sqrt{2}}$. Qubit 𝑎 is allocated to Alice, while qubit 𝑏 is designated for Bob.\n",
    "\n",
    "*Hint*: This state can be generated using a Hadamard gate and a CNOT gate."
   ]
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   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit.circuit import Qubit, Clbit\n",
    "\n",
    "\n",
    "def create_bell_pair(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:\n",
    "    \"\"\"Creates a bell pair between qubits a and b.\"\"\"\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    # unpack qubits\n",
    "    # the first qubit is s but we won't be using it in this exercise\n",
    "    _, a, b = qr\n",
    "\n",
    "    ####### your code goes here #######\n",
    "    qc.h(a)\n",
    "    qc.cx(a, b)\n",
    "\n",
    "    return qc"
   ]
  },
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   "execution_count": 6,
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\n",
      "text/plain": [
       "<Figure size 287.496x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qr = QuantumRegister(3, name=\"q\")\n",
    "cr = ClassicalRegister(3, name=\"c\")\n",
    "qc = create_bell_pair(qr, cr)\n",
    "\n",
    "qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d00f07df-5245-47c7-858b-3ae6cbafc475",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex2a\n",
    "\n",
    "grade_ex2a(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b5ec32-e8ae-4475-a0ea-5314acf49a4f",
   "metadata": {},
   "source": [
    "Let's assume Alice has qubit $a$ and Bob has qubit $b$ once they separate. Perhaps they really are having a relationship issue 😉."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba11110",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "Perform the next steps of the protocol:\n",
    "1. Alice applies a CNOT gate with $s$ (the qubit containing $\\lvert \\psi \\rangle$) as the control and $a$ as the target.\n",
    "2. Alice applies a Hadamard gate to $s$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34d7bcd6-166b-4745-8ede-f8aa1872d434",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def alice_gates(qr: QuantumRegister, cr: ClassicalRegister):\n",
    "    \"\"\"Creates Alices's gates\"\"\"\n",
    "    qc = create_bell_pair(qr, cr)\n",
    "    qc.barrier()  # Use barrier to separate steps\n",
    "    s, a, b = qr\n",
    "\n",
    "    ####### your code goes here #######\n",
    "    qc.cx(s, a)\n",
    "    qc.h(s)\n",
    "\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a5096db-1b92-45df-ac1b-52392356b543",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 538.33x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = alice_gates(qr, cr)\n",
    "qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66cead66-9c0e-4e7c-a196-efa456e6764b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex2b\n",
    "\n",
    "grade_ex2b(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d45bf84-f32b-4dd9-8e73-b8ec920e8b39",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Exercise 3\n",
    "\n",
    "In this step, Alice performs a measurement on both qubits in her possession and saves the results in two classical bits. Afterward, she sends these two bits to Bob.\n",
    "\n",
    "Complete the following code cell so that Alice measures qubit $a$ into classical bit $c0$ and qubit $s$ into classical bit $c1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "345bc52c-7745-46a3-90e7-a0e6a03604d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def measure_and_send(qr: QuantumRegister, cr: ClassicalRegister):\n",
    "    \"\"\"Measures qubits a & b and 'sends' the results to Bob\"\"\"\n",
    "    qc = alice_gates(qr, cr)\n",
    "    qc.barrier()  # Use barrier to separate steps\n",
    "    s, a, b = qr\n",
    "    c0, c1, c2 = cr\n",
    "\n",
    "    ####### your code goes here #######\n",
    "    qc.measure(a, c0)\n",
    "    qc.measure(s, c1)\n",
    "    \n",
    "    \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1c3ec5d-ce66-455c-a2fe-6cca03dd87f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 789.163x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = measure_and_send(qr, cr)\n",
    "qc.draw(\"mpl\", cregbundle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76b0c0b2-ed71-4eb7-b4d9-9b725a22fccb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex2c\n",
    "\n",
    "grade_ex2c(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fec0216-f312-430e-9940-0ab0ffd1beca",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "\n",
    "In this step, Bob, who is already in possession of qubit $b$, dynamically adds specific gates to the circuit based on the state of the classical bits received from Alice:\n",
    "\n",
    "- If the bits are `00`, no action is required.\n",
    "- If they are `01`, an 𝑋 gate (also known as a Pauli-X or a bit-flip gate) should be applied.\n",
    "- For bits `10`, a 𝑍 gate (also known as a Pauli-Z or a phase-flip gate) should be applied. \n",
    "- Lastly, if the classical bits are `11`, a combined 𝑍𝑋 gate should be applied, which involves applying both the 𝑍 and 𝑋 gates in sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e61ddfa4-e536-46dd-b5a7-efe15f3ad617",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bob_gates(qr: QuantumRegister, cr: ClassicalRegister):\n",
    "    \"\"\"Uses qc.if_test to control which gates are dynamically added\"\"\"\n",
    "    qc = measure_and_send(qr, cr)\n",
    "    qc.barrier()  # Use barrier to separate steps\n",
    "    s, a, b = qr\n",
    "    c0, c1, c2 = cr\n",
    "\n",
    "    ####### your code goes here #######\n",
    "    with qc.if_test((c0, 1)):\n",
    "        qc.x((0, 1, 2))\n",
    "    \n",
    "    with qc.if_test((c1, 0)):\n",
    "        qc.z((0, 1, 2))\n",
    "        \n",
    "\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff619db6-282a-450e-9b7c-38b2838cfdeb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1207.22x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = bob_gates(qr, cr)\n",
    "qc.draw(\"mpl\", cregbundle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ab0270f-f909-4239-bb52-e36410497c21",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "# Submit your circuit\n",
    "\n",
    "from qc_grader.challenges.spring_2023 import grade_ex2d\n",
    "\n",
    "grade_ex2d(qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74dc4ac1-d643-468e-a13c-509e2a12d77b",
   "metadata": {},
   "source": [
    "We will now have Bob measure his qubit into classical bit $c2$. After repeating the entire experiment multiple times, we can gather statistics on the measurement results to confirm that the teleportation worked correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6223a197-48b8-4b7b-889c-4bf317793a27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1290.83x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teleportation_circuit = bob_gates(qr, cr)\n",
    "s, a, b = qr\n",
    "c0, c1, c2 = cr\n",
    "teleportation_circuit.measure(b, c2)\n",
    "teleportation_circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3780a15e-4a86-4d5a-af4c-8662f4462fb9",
   "metadata": {},
   "source": [
    "Now that we have a teleportation circuit, let's ~~beam Captain Kirk to the surface of a strange planet~~ create and teleport a quantum state, and then run the circuit on a simulator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977a9fe6",
   "metadata": {},
   "source": [
    "### Exercise 5\n",
    "\n",
    "In the following code cell, construct a full quantum teleportation circuit into the `teleport_superposition_circuit` variable, using the following steps:\n",
    "\n",
    "- Construct a state preparation circuit. Prepare the qubit $s$ by applying an $R_x$ rotation with angle $\\pi / 4$.\n",
    "- Combine the state preparation circuit with your previously built teleportation circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "89fa5bdb-eb09-4fb8-9913-6fd6bb9f8ad2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1290.83x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "teleport_superposition_circuit: QuantumCircuit\n",
    "\n",
    "####### your code goes here #######\n",
    "\n",
    "state_preparation_circuit = QuantumCircuit(qr , cr) \n",
    "# qr: QuantumRegister\n",
    "# cr: ClassicalRegister\n",
    "    \n",
    "state_preparation_circuit.rx(math.pi/4, qr[0])\n",
    "teleport_superposition_circuit = state_preparation_circuit.compose(teleportation_circuit)\n",
    "\n",
    "\n",
    "# Uncomment this line to draw your circuit\n",
    "teleport_superposition_circuit.draw(\"mpl\", cregbundle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1e0ef91c-964a-4fd6-ab52-36c859b69b13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import transpile\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "sim = AerSimulator()\n",
    "transpiled_circuit = transpile(teleport_superposition_circuit, sim)\n",
    "\n",
    "# run job\n",
    "shots = 1000\n",
    "job = sim.run(transpiled_circuit, shots=shots, dynamic=True)\n",
    "\n",
    "# Get the results and display them\n",
    "exp_result = job.result()\n",
    "exp_counts = exp_result.get_counts()\n",
    "plot_histogram(exp_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e72c0a-8a4e-4d29-92a7-022d3882552a",
   "metadata": {},
   "source": [
    "Let's compute the distribution of just Bob's measurement by marginalizing over the other measured bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96a1fd3b-693b-4cd4-bb51-96f676dd1759",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trace out Bob's results on qubit 2\n",
    "from qiskit.result import marginal_counts\n",
    "\n",
    "bobs_counts = marginal_counts(exp_counts, [qr.index(b)])\n",
    "plot_histogram(bobs_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a33717b",
   "metadata": {},
   "source": [
    "The marginal distribution should be somewhat close to the ideal probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "475e3774-da15-483b-9d2d-af54a8e0e568",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "Congratulations 🎉! Your answer is correct and has been submitted.\n"
     ]
    }
   ],
   "source": [
    "from qc_grader.challenges.spring_2023 import grade_ex2e\n",
    "\n",
    "grade_ex2e(bobs_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9baa3099-7577-4bf9-9d1f-e0c54dc384b8",
   "metadata": {},
   "source": [
    "Now that we're fairly certain that ~~Captain Kirk~~ Alice's qubit will teleport safely, let's execute the quantum teleportation circuit on real hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca2f737c-a21f-4727-b14d-0e7e315631ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_ibm_provider import IBMProvider\n",
    "\n",
    "provider = IBMProvider()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da33a9fb-6b6c-48a4-a1b0-dfca70474e22",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hub = \"YOUR_HUB\"\n",
    "group = \"YOUR_GROUP\"\n",
    "project = \"YOUR_PROJECT\"\n",
    "\n",
    "backend_name = \"ibm_peekskill\"\n",
    "backend = provider.get_backend(backend_name, instance=f\"{hub}/{group}/{project}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe48f446-849f-46e9-b0a4-1dd5225db403",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# backend.target.add_instruction(IfElseOp, name=\"if_else\") # Uncomment if necessary\n",
    "qc_transpiled = transpile(teleport_superposition_circuit, backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cee84182-a90d-44b7-b44d-87309202cbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "job = backend.run(qc_transpiled, shots=1000, dynamic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334ff2bd",
   "metadata": {},
   "source": [
    "Because it takes time to run on the real backend, you typically use job_id to call in jobs after a time.\n",
    "The following code invokes jobs through job_id and checks the execution status. Here's how to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de24e832",
   "metadata": {},
   "outputs": [],
   "source": [
    "retrieve_job = provider.retrieve_job(job.job_id())\n",
    "retrieve_job.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab20774",
   "metadata": {},
   "source": [
    "If your job succesfully finished, let's import results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc8458f1-43fd-48b2-a50c-65771262cbf1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the results and display them\n",
    "exp_result = retrieve_job.result()\n",
    "exp_counts = exp_result.get_counts()\n",
    "plot_histogram(exp_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ddf3c8-732f-4825-a1b1-376bce0ab614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trace out Bob's results on qubit 2\n",
    "from qiskit.result import marginal_counts\n",
    "\n",
    "bobs_qubit = 2\n",
    "bobs_counts = marginal_counts(exp_counts, [bobs_qubit])\n",
    "plot_histogram(bobs_counts)"
   ]
  }
 ],
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