qiskit-documentation/docs/guides/debug-qiskit-runtime-jobs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c52e7bba-1230-4974-8e86-2dbe8f6f219b",
   "metadata": {},
   "source": [
    "# Debug Qiskit Runtime jobs\n",
    "{/* cspell:ignore ZIIIII, IZIIII,IIZIII, IIIZII, IIIIZI, IIIIIZ, rdiff */}\n",
    "\n",
    "Before submitting a resource-intensive Qiskit Runtime workload to execute on hardware, you can use the the Qiskit Runtime [`Neat` (Noisy Estimator Analyzer Tool)](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.debug_tools.Neat#neat) class to verify that your Estimator workload is set up correctly, is likely to return  accurate results, uses the most appropriate options for the specified problem, and more.\n",
    "\n",
    "`Neat` Cliffordizes the input circuits for efficient simulation, while retaining its structure and depth. Clifford circuits suffer similar levels of noise and are a good proxy for studying the original circuit of interest.\n",
    "\n",
    "\n",
    "The following examples illustrate situations where `Neat` can be a useful resource."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc5bf2a-e536-4141-a77c-0ee407cbd9b2",
   "metadata": {},
   "source": [
    "First, import the relevant packages and [authenticate to the Qiskit Runtime service.](/guides/setup-channel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d653e186-7ec3-4f1b-b0e9-b322055dd6c8",
   "metadata": {},
   "source": [
    "## Prepare the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f28c824-3158-43e6-ab3c-fd96c31859f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "from qiskit.circuit import QuantumCircuit\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
    "from qiskit_ibm_runtime.debug_tools import Neat\n",
    "\n",
    "from qiskit_aer.noise import NoiseModel, depolarizing_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a45a6d9e-de39-4586-8395-a7f580f0e0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the least busy backend\n",
    "service = QiskitRuntimeService()\n",
    "backend = service.least_busy(operational=True, simulator=False)\n",
    "\n",
    "# Generate a preset pass manager\n",
    "# This will be used to convert the abstract circuit to an equivalent Instruction Set Architecture (ISA) circuit.\n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=0)\n",
    "\n",
    "# Set the random seed\n",
    "random.seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67572a70-da01-40fe-b299-b5599561164a",
   "metadata": {},
   "source": [
    "## Initialize a target circuit\n",
    "\n",
    "Consider a six-qubit circuit that has the following properties:\n",
    "\n",
    "* Alternates between random `RZ` rotations and layers of `CNOT` gates.\n",
    "* Has a mirror structure, that is, it applies a unitary `U` followed by its inverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df19af55-897d-4b1f-baf8-fac2641ae87d",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1123.61x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_circuit(n_qubits, n_layers):\n",
    "    r\"\"\"\n",
    "    A function to generate a pseudo-random a circuit with ``n_qubits`` qubits and\n",
    "    ``2*n_layers`` entangling layers of the type used in this notebook.\n",
    "    \"\"\"\n",
    "    # An array of random angles\n",
    "    angles = [\n",
    "        [random.random() for q in range(n_qubits)] for s in range(n_layers)\n",
    "    ]\n",
    "\n",
    "    qc = QuantumCircuit(n_qubits)\n",
    "    qubits = list(range(n_qubits))\n",
    "\n",
    "    # do random circuit\n",
    "    for layer in range(n_layers):\n",
    "        # rotations\n",
    "        for q_idx, qubit in enumerate(qubits):\n",
    "            qc.rz(angles[layer][q_idx], qubit)\n",
    "\n",
    "        # cx gates\n",
    "        control_qubits = (\n",
    "            qubits[::2] if layer % 2 == 0 else qubits[1 : n_qubits - 1 : 2]\n",
    "        )\n",
    "        for qubit in control_qubits:\n",
    "            qc.cx(qubit, qubit + 1)\n",
    "\n",
    "    # undo random circuit\n",
    "    for layer in range(n_layers)[::-1]:\n",
    "        # cx gates\n",
    "        control_qubits = (\n",
    "            qubits[::2] if layer % 2 == 0 else qubits[1 : n_qubits - 1 : 2]\n",
    "        )\n",
    "        for qubit in control_qubits:\n",
    "            qc.cx(qubit, qubit + 1)\n",
    "\n",
    "        # rotations\n",
    "        for q_idx, qubit in enumerate(qubits):\n",
    "            qc.rz(-angles[layer][q_idx], qubit)\n",
    "\n",
    "    return qc\n",
    "\n",
    "\n",
    "# Generate a random circuit\n",
    "qc = generate_circuit(6, 3)\n",
    "# Convert the abstract circuit to an equivalent ISA circuit.\n",
    "isa_qc = pm.run(qc)\n",
    "\n",
    "qc.draw(\"mpl\", idle_wires=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0167329b-c6a6-4b2c-98fc-bf9aba9b7ee6",
   "metadata": {},
   "source": [
    "Choose single-Pauli `Z` operators as observables and use them to initialize the primitive unified blocs (PUBs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "830b1dcc-2669-46cc-bff8-01a96a05c6ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observables: ['ZIIIII', 'IZIIII', 'IIZIII', 'IIIZII', 'IIIIZI', 'IIIIIZ']\n"
     ]
    }
   ],
   "source": [
    "# Initialize the observables\n",
    "obs = [\"ZIIIII\", \"IZIIII\", \"IIZIII\", \"IIIZII\", \"IIIIZI\", \"IIIIIZ\"]\n",
    "print(f\"Observables: {obs}\")\n",
    "\n",
    "# Map the observables to the backend's layout\n",
    "isa_obs = [SparsePauliOp(o).apply_layout(isa_qc.layout) for o in obs]\n",
    "\n",
    "# Initialize the PUBs, which consist of six-qubit circuits with `n_layers` 1, ..., 6\n",
    "all_n_layers = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "pubs = [(pm.run(generate_circuit(6, n)), isa_obs) for n in all_n_layers]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a49fc84-0c82-4cbb-a557-6e676e57c9fa",
   "metadata": {},
   "source": [
    "## Cliffordize the circuits\n",
    "\n",
    "The previously defined PUB circuits are not Clifford, which makes them difficult to simulate classically. However, you can use the `Neat` [`to_clifford`](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.debug_tools.Neat#to_clifford) method to map them to Clifford circuits for more efficient simulation.  The [`to_clifford`](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.debug_tools.Neat#to_clifford) method is a wrapper around the [`ConvertISAToClifford`](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.transpiler.passes.ConvertISAToClifford) transpiler pass, which can also be used independently. In particular, it replaces non-Clifford single-qubit gates in the original circuit with Clifford single-qubit gates, but it does not mutate the two-qubit gates, number of qubits, or circuit depth.\n",
    "\n",
    "See [Efficient simulation of stabilizer circuits with Qiskit Aer primitives](/guides/simulate-stabilizer-circuits) for more information about Clifford circuit simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a86d99e-4431-4d62-8227-c49d17856369",
   "metadata": {},
   "source": [
    "First, initialize `Neat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b5bbd4c-bd7f-4679-9348-d41da74d26eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You could specify a custom `NoiseModel` here. If `None`, `Neat`\n",
    "# pulls the noise model from the given backend\n",
    "noise_model = None\n",
    "\n",
    "# Initialize `Neat`\n",
    "analyzer = Neat(backend, noise_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b740dcdf-660e-41e2-b5e6-e8cc288af38b",
   "metadata": {},
   "source": [
    "Next, Cliffordize the PUBs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ad78f41-a2f8-4381-826a-ae728e081ad6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2210.55x1120.39 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clifford_pubs = analyzer.to_clifford(pubs)\n",
    "\n",
    "clifford_pubs[0].circuit.draw(\"mpl\", idle_wires=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c3ff81-9f18-43eb-ba6e-57c5ef3d118f",
   "metadata": {},
   "source": [
    "## Application 1: Analyze the impact of noise on the circuit outputs\n",
    "\n",
    "This example shows how to use `Neat` to study the impact of different noise models on PUBs as a function of circuit depth by running simulations in both ideal ([`ideal_sim`](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.debug_tools.Neat#ideal_sim)) and noisy ([`noisy_sim`](/api/qiskit-ibm-runtime/qiskit_ibm_runtime.debug_tools.Neat#noisy_sim)) conditions. This can be useful to set up expectations on the quality of the experimental results before running a job on a QPU. To learn more about noise models, see [Exact and noisy simulation with Qiskit Aer primitives.](/guides/simulate-with-qiskit-aer#exact-and-noisy-simulation-with-qiskit-aer-primitives)\n",
    "\n",
    "The simulated results support mathematical operations, and can therefore be compared with each other (or with experimental results) to calculate figures of merit.\n",
    "\n",
    "Begin by performing ideal and noisy classical simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23859a99-2455-460e-98ea-17b36ea59c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal results:\n",
      " NeatResult([NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.])), NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.])), NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.])), NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.])), NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.])), NeatPubResult(vals=array([1., 1., 1., 1., 1., 1.]))])\n",
      "\n",
      "Noisy results:\n",
      " NeatResult([NeatPubResult(vals=array([0.96875   , 0.97265625, 0.94921875, 0.95117188, 0.97070312,\n",
      "       0.97460938])), NeatPubResult(vals=array([0.93945312, 0.9453125 , 0.90429688, 0.94335938, 0.96484375,\n",
      "       0.98046875])), NeatPubResult(vals=array([0.87890625, 0.875     , 0.82226562, 0.86523438, 0.95898438,\n",
      "       0.94726562])), NeatPubResult(vals=array([0.80273438, 0.82226562, 0.79492188, 0.828125  , 0.89257812,\n",
      "       0.9375    ])), NeatPubResult(vals=array([0.73242188, 0.74023438, 0.6953125 , 0.7578125 , 0.8828125 ,\n",
      "       0.92382812])), NeatPubResult(vals=array([0.70898438, 0.72070312, 0.6953125 , 0.77539062, 0.88085938,\n",
      "       0.90625   ]))])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform a noiseless simulation\n",
    "ideal_results = analyzer.ideal_sim(clifford_pubs)\n",
    "print(f\"Ideal results:\\n {ideal_results}\\n\")\n",
    "\n",
    "# Perform a noisy simulation with the backend's noise model\n",
    "noisy_results = analyzer.noisy_sim(clifford_pubs)\n",
    "print(f\"Noisy results:\\n {noisy_results}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a000a77a-0285-4b72-a69f-8f144f2c2a80",
   "metadata": {},
   "source": [
    "Next, apply mathematical operations to compute the absolute difference. The remainder of the guide uses the absolute difference as a figure of merit to compare ideal results with noisy or experimental results, but similar figures of merit can be set up.\n",
    "\n",
    "The absolute difference shows that the impact of noise grows with the circuits' sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd61e437-bd2f-4349-a667-7edab51c4a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute difference between ideal and noisy results for circuits with 1 layers:\n",
      "  3.55%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 2 layers:\n",
      "  5.37%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 3 layers:\n",
      "  10.87%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 4 layers:\n",
      "  15.36%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 5 layers:\n",
      "  21.13%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 6 layers:\n",
      "  21.88%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Figure of merit: Absolute difference\n",
    "def rdiff(res1, re2):\n",
    "    r\"\"\"The absolute difference between `res1` and re2`.\n",
    "\n",
    "    --> The closer to `0`, the better.\n",
    "    \"\"\"\n",
    "    d = abs(res1 - re2)\n",
    "    return np.round(d.vals * 100, 2)\n",
    "\n",
    "\n",
    "for idx, (ideal_res, noisy_res) in enumerate(\n",
    "    zip(ideal_results, noisy_results)\n",
    "):\n",
    "    vals = rdiff(ideal_res, noisy_res)\n",
    "\n",
    "    # Print the mean absolute difference for the observables\n",
    "    mean_vals = np.round(np.mean(vals), 2)\n",
    "    print(\n",
    "        f\"Mean absolute difference between ideal and noisy results for circuits with {all_n_layers[idx]} layers:\\n  {mean_vals}%\\n\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7abcd001-9eac-4015-97a3-d6250ea4b667",
   "metadata": {},
   "source": [
    "You can follow these rough and simplified guidelines to improve circuits of this type:\n",
    "\n",
    "- If the mean absolute difference is greater than 90%, mitigation will likely not help.\n",
    "- If the mean absolute difference is less than 90%, [Probabilistic Error Amplification (PEA)](/guides/error-mitigation-and-suppression-techniques#probabilistic-error-amplification-pea) will likely be able to improve the results.\n",
    "- If the mean absolute difference is less than 80%, [ZNE with gate folding](/guides/error-mitigation-and-suppression-techniques#zero-noise-extrapolation-zne) will also likely be able to improve the results.\n",
    "\n",
    "Because all of the absolute differences above are less than 90%, applying PEA to the original circuit will hopefully improve the quality of its results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64c936b-5b8f-4fd2-861d-8b1ded2a0ad4",
   "metadata": {},
   "source": [
    "You can specify different noise models in the analyzer. The following example performs the same test but adds a custom noise model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0835c562-55c9-4dbe-879e-7271f8bed280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute difference between ideal and noisy results for circuits with 1 layers:\n",
      "  4.43%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 2 layers:\n",
      "  7.49%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 3 layers:\n",
      "  13.15%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 4 layers:\n",
      "  17.74%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 5 layers:\n",
      "  23.3%\n",
      "\n",
      "Mean absolute difference between ideal and noisy results for circuits with 6 layers:\n",
      "  29.43%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set up a noise model with strength 0.02 on every two-qubit gate\n",
    "noise_model = NoiseModel()\n",
    "for qubits in backend.coupling_map:\n",
    "    noise_model.add_quantum_error(\n",
    "        depolarizing_error(0.02, 2), [\"ecr\", \"cx\"], qubits\n",
    "    )\n",
    "\n",
    "# Update the analyzer's noise model\n",
    "analyzer.noise_model = noise_model\n",
    "\n",
    "# Perform a noiseless simulation\n",
    "ideal_results = analyzer.ideal_sim(clifford_pubs)\n",
    "\n",
    "# Perform a noisy simulation with the backend's noise model\n",
    "noisy_results = analyzer.noisy_sim(clifford_pubs)\n",
    "\n",
    "# Compare the results\n",
    "for idx, (ideal_res, noisy_res) in enumerate(\n",
    "    zip(ideal_results, noisy_results)\n",
    "):\n",
    "    values = rdiff(ideal_res, noisy_res)\n",
    "\n",
    "    # Print the mean absolute difference for the observables\n",
    "    mean_values = np.round(np.mean(values), 2)\n",
    "    print(\n",
    "        f\"Mean absolute difference between ideal and noisy results for circuits with {all_n_layers[idx]} layers:\\n  {mean_values}%\\n\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2408ca9-3e3c-4a2f-a99a-ce413d5d470f",
   "metadata": {},
   "source": [
    "As shown, given a noise model, you can try to quantify the impact of noise on the (Cliffordized version of the) PUBs of interest before running them on a QPU."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd6da5f-4e84-4bf4-aaeb-0403f21275db",
   "metadata": {},
   "source": [
    "## Application 2: Benchmark different strategies\n",
    "\n",
    "This example uses `Neat` to help identify the best options for your PUBs. To do so, consider running an estimation problem with PEA, which cannot be simulated with `qiskit_aer`. You can use `Neat` to help determine which noise amplification factors will work best, then use those factors when running the original experiment on a QPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "358bb82a-4bc9-46c2-98a0-e745ffc6788f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a circuit with six qubits and six layers\n",
    "isa_qc = pm.run(generate_circuit(6, 3))\n",
    "\n",
    "# Use the same observables as previously\n",
    "pubs = [(isa_qc, isa_obs)]\n",
    "clifford_pubs = analyzer.to_clifford(pubs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5774cb3f-c999-4242-a83a-7dcc0c57510b",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_factors = [\n",
    "    [1, 1.1],\n",
    "    [1, 1.1, 1.2],\n",
    "    [1, 1.5, 2],\n",
    "    [1, 1.5, 2, 2.5, 3],\n",
    "    [1, 4],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b9900e6-84fe-4776-9bb5-08c6c729be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the PUBs on a QPU\n",
    "estimator = Estimator(backend)\n",
    "estimator.options.default_shots = 100000\n",
    "estimator.options.twirling.enable_gates = True\n",
    "estimator.options.twirling.enable_measure = True\n",
    "estimator.options.twirling.shots_per_randomization = 100\n",
    "estimator.options.resilience.measure_mitigation = True\n",
    "estimator.options.resilience.zne_mitigation = True\n",
    "estimator.options.resilience.zne.amplifier = \"pea\"\n",
    "\n",
    "jobs = []\n",
    "for factors in noise_factors:\n",
    "    estimator.options.resilience.zne.noise_factors = factors\n",
    "    jobs.append(estimator.run(clifford_pubs))\n",
    "\n",
    "results = [job.result() for job in jobs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16c18377-059a-4751-9ab1-afee0ed5b089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a noiseless simulation\n",
    "ideal_results = analyzer.ideal_sim(clifford_pubs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7db531a1-c417-4d5b-bdc3-7a4ad3385fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute difference for factors [1, 1.1]:\n",
      "  18.86%\n",
      "\n",
      "Mean absolute difference for factors [1, 1.1, 1.2]:\n",
      "  22.92%\n",
      "\n",
      "Mean absolute difference for factors [1, 1.5, 2]:\n",
      "  29.36%\n",
      "\n",
      "Mean absolute difference for factors [1, 1.5, 2, 2.5, 3]:\n",
      "  22.38%\n",
      "\n",
      "Mean absolute difference for factors [1, 4]:\n",
      "  29.36%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Look at the mean absolute difference to quickly tell the best choice for your options\n",
    "for factors, res in zip(noise_factors, results):\n",
    "    d = rdiff(ideal_results[0], res[0])\n",
    "    print(\n",
    "        f\"Mean absolute difference for factors {factors}:\\n  {np.round(np.mean(d), 2)}%\\n\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c37ef7b-df56-4f5f-9e11-10f209f105f9",
   "metadata": {},
   "source": [
    "The result with the smallest difference suggests which options to choose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2530a3e9-21a6-4841-9449-fe181c54aca4",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
    "    - Learn about [Exact and noisy simulation with Qiskit Aer primitives.](https://docs.quantum.ibm.com/guides/simulate-with-qiskit-aer)\n",
    "    - Learn about [available Qiskit Runtime options.](/guides/runtime-options-overview)\n",
    "    - Learn about [Error mitigation and suppression techniques.](/guides/error-mitigation-and-suppression-techniques)\n",
    "    - Visit the [Transpile with pass managers](transpile-with-pass-managers) topic.\n",
    "    - Learn [how to transpile circuits](https://learning.quantum.ibm.com/tutorial/submit-transpiled-circuits) as part of Qiskit Patterns workflows using Qiskit Runtime.\n",
    "    - Review the [Debugging tools API documentation.](/api/qiskit-ibm-runtime/debug_tools)\n",
    "</Admonition>"
   ]
  }
 ],
 "metadata": {
  "description": "Use the Qiskit Runtime Debugging tools module and `Neat` class to debug and analyze jobs.",
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