Exercise 3-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc712e3b-05bc-4c55-85fd-892fb79510c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/opt/conda/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/opt/conda/lib/python3.8/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/opt/conda/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52fdf781-ead7-4b57-85a7-481b3558dc70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise 3-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e099c359-d217-41a5-8458-aca45f4caa77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_155/2783433861.py:16: DeprecationWarning: The QuantumCircuit.u1 method is deprecated as of 0.16.0. It will be removed no earlier than 3 months after the release date. You should use the QuantumCircuit.p method instead, which acts identically.\n",
      "  qc.u1(math.pi,3)\n",
      "/tmp/ipykernel_155/2783433861.py:17: DeprecationWarning: The QuantumCircuit.u2 method is deprecated as of 0.16.0. It will be removed no earlier than 3 months after the release date. You can use the general 1-qubit gate QuantumCircuit.u instead: u2(φ,λ) = u(π/2, φ, λ). Alternatively, you can decompose it interms of QuantumCircuit.p and QuantumCircuit.sx: u2(φ,λ) = p(π/2+φ) sx p(λ-π/2) (1 pulse on hardware).\n",
      "  qc.u2(math.pi,math.pi/2,4)\n",
      "/tmp/ipykernel_155/2783433861.py:18: DeprecationWarning: The QuantumCircuit.u3 method is deprecated as of 0.16.0. It will be removed no earlier than 3 months after the release date. You should use QuantumCircuit.u instead, which acts identically. Alternatively, you can decompose u3 in terms of QuantumCircuit.p and QuantumCircuit.sx: u3(ϴ,φ,λ) = p(φ+π) sx p(ϴ+π) sx p(λ) (2 pulses on hardware).\n",
      "  qc.u3(math.pi,math.pi/2,math.pi/4,5)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 327.397x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "import math\n",
    "qc = QuantumCircuit(6)\n",
    "qc.rx(math.pi/2,0)\n",
    "qc.p(math.pi/4,1)\n",
    "qc.rx(math.pi/2,2)\n",
    "qc.p(math.pi/4,3)\n",
    "qc.rx(math.pi/2,4)\n",
    "qc.p(math.pi/4,5)\n",
    "qc.x(0)\n",
    "qc.y(1)\n",
    "qc.z(2)\n",
    "qc.s(3)\n",
    "qc.t(4)\n",
    "qc.h(5)\n",
    "qc.u1(math.pi,3)\n",
    "qc.u2(math.pi,math.pi/2,4)\n",
    "qc.u3(math.pi,math.pi/2,math.pi/4,5)\n",
    "qc.h(5)\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3dc58c4-1fc2-47de-a7f8-2cad33895c7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Qiskit v0.35.0 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}