Terra 0.19 release.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's new in Qiskit 0.33 (Terra 0.19)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** This is a *tiny excerpt* of all the changes that might be particularly interesting for the application modules. All changes with detailed explanations can be found here: https://qiskit.org/documentation/release_notes.html#terra-0-19-0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removal of Qiskit Aqua\n",
    "\n",
    "Aqua is no longer included in the metapackage. That means `pip install qiskit` will not install Aqua (but not remove any exisiting installations)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer signature\n",
    "\n",
    "The Qiskit optimizers changed from\n",
    "```\n",
    "Optimizer.optimize(num_vars, objective_function, gradient_function, variable_bounds, initial_point) -> Tuple\n",
    "```\n",
    "to \n",
    "```\n",
    "Optimizer.minimize(fun, x0, jac, bounds) -> OptimizerResult\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "\n",
    "def f(x):\n",
    "    return (np.linalg.norm(x) - 1) ** 2\n",
    "\n",
    "initial_point = np.array([0.2, 1.5])\n",
    "optimizer = COBYLA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "(array([-0.73409049,  0.67888771]), 1.2391749693729586e-08, 35)\n"
     ]
    }
   ],
   "source": [
    "result = optimizer.optimize(len(initial_point), f, initial_point=initial_point)\n",
    "\n",
    "print(type(result))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'qiskit.algorithms.optimizers.optimizer.OptimizerResult'>\n",
      "{   'fun': 1.2391749693729586e-08,\n",
      "    'jac': None,\n",
      "    'nfev': 35,\n",
      "    'nit': None,\n",
      "    'njev': None,\n",
      "    'x': array([-0.73409049,  0.67888771])}\n"
     ]
    }
   ],
   "source": [
    "result = optimizer.minimize(f, initial_point)\n",
    "\n",
    "print(type(result))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic circuits\n",
    "\n",
    "We can now build circuits with classical control flow, such as for-loops and if-statements. They can be exported to OpenQASM3, but not yet executed within Qiskit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "\n",
    "qc = QuantumCircuit(2, 1)\n",
    "\n",
    "with qc.for_loop(range(5)) as i:\n",
    "    qc.h(0)\n",
    "    qc.cx(0, 1)\n",
    "    qc.measure(0, 0)\n",
    "    qc.break_loop().c_if(0, val=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jul/Qiskit/qiskit-terra/qiskit/visualization/text.py:665: RuntimeWarning: The parameter \"cregbundle\" was disabled, since an instruction needs to refer to individual classical wires\n",
      "  return \"\\n\".join(self.lines()).encode(self.encoding).decode(self.encoding)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───────────┐\n",
       "q_0: ┤0          ├\n",
       "     │           │\n",
       "q_1: ┤1 For_loop ├\n",
       "     │           │\n",
       "  c: ╡0          ╞\n",
       "     └───────────┘</pre>"
      ],
      "text/plain": [
       "     ┌───────────┐\n",
       "q_0: ┤0          ├\n",
       "     │           │\n",
       "q_1: ┤1 For_loop ├\n",
       "     │           │\n",
       "  c: ╡0          ╞\n",
       "     └───────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENQASM 3;\n",
      "include \"stdgates.inc\";\n",
      "bit[1] c;\n",
      "qubit[2] _all_qubits;\n",
      "let q = _all_qubits[0:1];\n",
      "for _ in [0:4] {\n",
      "  h q[0];\n",
      "  cx q[0], q[1];\n",
      "  c[0] = measure q[0];\n",
      "  break;\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from qiskit.qasm3 import dumps\n",
    "\n",
    "openqasm3 = dumps(qc)\n",
    "print(openqasm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two step transpilation\n",
    "\n",
    "In variational algorithms: Transpile the parameterized circuit once *and* the bound circuit in each iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "from qiskit.utils import QuantumInstance\n",
    "\n",
    "unbound_pass_manager = # some general optimizations: gate commutativites, reduce 1q gates, ...\n",
    "bound_pass_manager = # parameter-specific optimization: efficient pulse decompositions\n",
    "\n",
    "quantum_instance = QuantumInstance(\n",
    "    pass_manager=unbound_pass_manager,\n",
    "    bound_pass_manager=bound_pass_manager,\n",
    "    ...\n",
    ")   \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.test.mock import FakeBelem\n",
    "from qiskit.transpiler import PassManagerConfig\n",
    "from qiskit.transpiler.preset_passmanagers import level_1_pass_manager\n",
    "\n",
    "backend = FakeBelem()\n",
    "\n",
    "unbound_pm = level_1_pass_manager(PassManagerConfig.from_backend(FakeBelem()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library.standard_gates.equivalence_library import StandardEquivalenceLibrary as std_eqlib\n",
    "from qiskit.transpiler import PassManager\n",
    "from qiskit.transpiler.passes import (\n",
    "  Collect2qBlocks, ConsolidateBlocks, Optimize1qGatesDecomposition,\n",
    "  RZXCalibrationBuilderNoEcho, UnrollCustomDefinitions, BasisTranslator\n",
    ")\n",
    "from qiskit.transpiler.passes.optimization.echo_rzx_weyl_decomposition import EchoRZXWeylDecomposition\n",
    "\n",
    "# Build the pass manager for the parameterized circuit\n",
    "rzx_basis = ['rzx', 'rz', 'x', 'sx']\n",
    "\n",
    "# Build a pass manager for the CX decomposition (works only on bound circuits)\n",
    "bound_pm = PassManager([\n",
    "  # Consolidate consecutive two-qubit operations.\n",
    "  Collect2qBlocks(),\n",
    "  ConsolidateBlocks(basis_gates=['rz', 'sx', 'x', 'rxx']),\n",
    "\n",
    "  # Rewrite circuit in terms of Weyl-decomposed echoed RZX gates.\n",
    "  EchoRZXWeylDecomposition(backend.defaults().instruction_schedule_map),\n",
    "\n",
    "  # Attach scaled CR pulse schedules to the RZX gates.\n",
    "  RZXCalibrationBuilderNoEcho(backend),\n",
    "\n",
    "  # Simplify single-qubit gates.\n",
    "  UnrollCustomDefinitions(std_eqlib, rzx_basis),\n",
    "  BasisTranslator(std_eqlib, rzx_basis),\n",
    "  Optimize1qGatesDecomposition(rzx_basis),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from qiskit.algorithms import VQE\n",
    "from qiskit.opflow import Z\n",
    "from qiskit.utils import QuantumInstance\n",
    "\n",
    "quantum_instance = QuantumInstance(\n",
    "    backend, pass_manager=unbound_pm, bound_pass_manager=bound_pm\n",
    ")\n",
    "\n",
    "vqe = VQE(quantum_instance=quantum_instance)\n",
    "result = vqe.compute_minimum_eigenvalue(Z ^ Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pauli evolutions\n",
    "\n",
    "The `PauliEvolutionGate` is a new gate for the time evolution (sums of) Paulis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import PauliEvolutionGate\n",
    "from qiskit.opflow import X, Z, I\n",
    "\n",
    "op = 0.2 * (Z ^ Z) + (I ^ X) + (X ^ I)\n",
    "time = 2\n",
    "\n",
    "evo = PauliEvolutionGate(op, time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 327.252x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "\n",
    "circuit = QuantumCircuit(2)\n",
    "circuit.append(evo, circuit.qubits)\n",
    "circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per default, a Lie-Trotter decomposition is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 447.652x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.decompose().draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but by specifying the `synthesis` argument we can select the decomposition. For instance, we can use 4-th order Trotter decomposition with 2 repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.synthesis import SuzukiTrotter\n",
    "\n",
    "evo = PauliEvolutionGate(op, time, synthesis=SuzukiTrotter(order=4, reps=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1591.45x866.88 with 1 Axes>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = QuantumCircuit(2)\n",
    "circuit.append(evo, circuit.qubits)\n",
    "circuit.decompose().draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mKSYyQdPnpSglabzahnX0cFLUZ/9mSQ7VueMqSQe+k+Ku8kQi+9qXrXrb2u0yrpvU1eYH1TKG2Me+7PqXOX1COr5XahPT8nkAAADgvby2+CVJUVFRVaYXFxcrIyND1113XcW0lJSURr1GRoZxjo/D4WhkSs+5e+z/atLVv6s7q1v6Zk22kif28VywZpb2ixXq1zO1Sb/j/VXzFR+drISYZEnSXWPm6LmlD2nW5IV1Pi8jI11XTBrZpNfGxXns3g91ecK18vNz1rpMeXmZXpj3mtJG2Le47QkvPLxRsZ361rmM2+3Wn2Y9oX8ts+7F3c0aQxg/PM/P4aeP55Y3aNkbx92ilZv+08KJAAAAYAZ3Aw/z98riV2RkpCQpJydH48aNq5g+d+5c7d+/X8nJyWZFM8XxoiP1FulcrnIVnH8lax81fuj9VX4e2vvHGtr7x+aEQZ1OFB2Ru57Dvhx+fjpexHmPTXW88JDKXeVy1lFodDgcOn6KtmYMsQaX26XC4gK1btWm3mUZQwAAAOBwN7RM5kEul0sDBgzQ/v37lZaWpujoaC1ZskQffvih8vLytHr1ag0ePLjKc1JTU/XAAw/olltuMSl1yzl9UvryH6r39LBeY6TO1j3wS1mLpIJ8c167TYx0+W3mvLavOrJL2tCAgzEG3ymFdWj5PHa2b5P03cf1LOSQhk2RgsM8EqlFmDWGMH6YY+tyKX993csEtTbWa1+7kykAAACq8srNQT8/Py1evFhJSUmaNm2a7r77bkVGRur++++X0+msuNi9rwgOk2Lq+pMdUqsIqeOlHosENFn77lJ4VN3LRPak8NUcOl4qtWoj47pftYjua+3CF3xP12TJGaA61+vYFApfAAAA8NLilyQlJCRoxYoVKioqUl5enubMmaNNmzapV69eatWqldnxPC7hainq/IvZO1SxwR/SVho48exOAGARDofU/ydSeKdzE1RlvW7fXep9vTnZ7MYZIA2cYIwVkqq0syRFXSYlXm1GMqDxQtpIA26R/IPOTrhgve45zCjqAgAAAF552mNtLrvsMl155ZV66aWXKqbNnj1bL730kg4fPqzWrVurVatWysjIUM+ePU1M2nJOHDTucHX6hOQfKHVIlCJjJT+vLWM2XG2nLM1fOkM5+VmKix6o+8c/XTE9Y+NiLc74qxxyaNLVv1NK7/F6aslU7TqQLYfDoV/e9JxiO/fVjr0b9Mw798vPz0/3jH1cfWKHV3sNTlsyj9stHd0tHdginTktBYVKnZKkiM5GgQzNx+WSjuyUDm2TykqNI70695HCbXIjQ7PGEMYPc5WXSge2Sj/kGnctDY2UovsYR0QDAAAAkpde8L4mhYWFysnJ0X333Vdl+pw5czRnzhyTUnleeEf77Kg2xPb8dSouKdST963U0/+Zpm171iqxyxWSpLdXPqm0X6TL4XDoty+OVUrv8br16v9Rp3Y9lH94uxZ8+D/640//o1c++YN+f8dbCgtppz+98hM9EbvM5L8K53M4jKO82nc3O4n9+flJHeKMh69gDLE/Z6BxhBdHeQEAAKA2lil+tW7dWuXlDbutOexjS95qJSdcI0kaGD9a3+1eVbHj2ql9T50uLZIkhQaFG9Pa9ZAk+TsD5Hf2znaFxcd0SZsYSdLpM0UqOVOsoADfO3UW8EWMIQAAAAAsU/yCbyosLlCndrGSpNDgCOUe3Fwxb2jvmzTtqQFyu92aOfGlKs9b8NFvddOwX0qSIkIv0a4D2WrbuqNyD2SrsLiAHVfARzCGAAAAAKD4Ba8WGhyhUyUnJElFJSfUulWbinmvf/pnvTjzO0nSrAXjdHnitZKkt1c+pW4deql3j2GSpJ+N+4ueeecBhQSFqUenvooIjfTsHwHANIwhAAAAAGxwmXTYWa9uQ7R++3JJ0vrtn+myrldWzAv0D1JwQIiCA0NVVl4qScra9ok252Zq8ujfVywXc0mC/m/KJ3rolhfUoU1X+XNbTMBnMIYAAAAA4MgveLX4mIEKCAjWjOeGq2fn/urQpqveWP6YJo+apR8NmaaH5g2VJI0bPEWSNO+9BxUSFK6Zz49Ul0sS9dAtL+ijrxdo+brXFRjQSg/eNM/MPweAhzGGAAAAAHC43W632SEAScpaJBXkm/PabWKky28z57UBNA+zxhDGDwAAAMC7cdojAAAAAAAAbIviFwAAAAAAAGyLa37Ba4R18M3XBtA8zOrHjB8AAACAd+OaXwAAAAAAALAtTnsEAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBtUfwCAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBtUfwCAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBtUfwCAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBtUfwCAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBtUfwCAAAAAACAbVH8AgAAAAAAgG1R/AIAAAAAAIBt/f9fThWPuy9PjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1591.45x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.decompose().decompose().draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}