diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index f2e43257c..14e64ba62 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -49,6 +49,8 @@ jobs:
           QISKIT_DOCS_BUILD_TUTORIALS: 'always'
         run: |
           echo "earliest_version: 0.1.0" >> releasenotes/config.yaml
+          # disable a PySCF UserWarning w.r.t. a change in the B3LYP DFT functional
+          echo "B3LYP_WITH_VWN5 = True" >> ~/.pyscf_conf.py
           tools/ignore_untagged_notes.sh
           make html
         shell: bash
diff --git a/docs/tutorials/01_electronic_structure.ipynb b/docs/tutorials/01_electronic_structure.ipynb
index db84e1b39..efd02f4b3 100644
--- a/docs/tutorials/01_electronic_structure.ipynb
+++ b/docs/tutorials/01_electronic_structure.ipynb
@@ -606,7 +606,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/02_vibrational_structure.ipynb b/docs/tutorials/02_vibrational_structure.ipynb
index 3150b09c9..b0ce5bbe6 100644
--- a/docs/tutorials/02_vibrational_structure.ipynb
+++ b/docs/tutorials/02_vibrational_structure.ipynb
@@ -731,7 +731,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/03_ground_state_solvers.ipynb b/docs/tutorials/03_ground_state_solvers.ipynb
index b7576c158..628ee0fdb 100644
--- a/docs/tutorials/03_ground_state_solvers.ipynb
+++ b/docs/tutorials/03_ground_state_solvers.ipynb
@@ -373,7 +373,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/04_excited_states_solvers.ipynb b/docs/tutorials/04_excited_states_solvers.ipynb
index c2cb66851..70d61ad77 100644
--- a/docs/tutorials/04_excited_states_solvers.ipynb
+++ b/docs/tutorials/04_excited_states_solvers.ipynb
@@ -421,7 +421,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/05_problem_transformers.ipynb b/docs/tutorials/05_problem_transformers.ipynb
index 1be924431..b14743e51 100644
--- a/docs/tutorials/05_problem_transformers.ipynb
+++ b/docs/tutorials/05_problem_transformers.ipynb
@@ -496,7 +496,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/06_qubit_mappers.ipynb b/docs/tutorials/06_qubit_mappers.ipynb
index 0b52f2d2d..a28f282ad 100644
--- a/docs/tutorials/06_qubit_mappers.ipynb
+++ b/docs/tutorials/06_qubit_mappers.ipynb
@@ -432,7 +432,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/07_leveraging_qiskit_runtime.ipynb b/docs/tutorials/07_leveraging_qiskit_runtime.ipynb
deleted file mode 100644
index 1b85c125a..000000000
--- a/docs/tutorials/07_leveraging_qiskit_runtime.ipynb
+++ /dev/null
@@ -1,510 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Leveraging Qiskit Runtime "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ".. warning::\n",
-    "    The `VQEClient` discussed in this tutorial is **DEPRECATED** as of version 0.6 of Qiskit Nature and will be removed no sooner than 3 months after the release. Instead you should use the new primitives based VQE with the Qiskit IBM Runtime Estimator primitive. For more details on how to migrate check out this guide [here](https://qisk.it/algo_migration#vqe)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Iterative algorithms, such as the Variational Quantum Eigensolver (VQE), traditionally send one batch of circuits (one \"job\") to be executed on the quantum device in each iteration. Sending a job involves certain overhead, mainly\n",
-    "\n",
-    "* the time to process the requests and send the data (API overhead, usually about 10s)\n",
-    "* the job queue time, that is how long you have to wait before it's your turn to run on the device (usually about 2min)\n",
-    "\n",
-    "If we send hundreds of jobs iteratively, this overhead quickly dominates the execution time of our algorithm.\n",
-    "Qiskit Runtime allows us to tackle these issues and significantly speed up (especially) iterative algorithms. With Qiskit Runtime, one job does not contain only a batch of circuits but the _entire_ algorithm. That means we only experience the API overhead and queue wait _once_ instead of in every iteration! You'll be able to either upload algorithm parameters and delegate all the complexity to the cloud, where your program is executed, or upload your personal algorithm directly.\n",
-    "\n",
-    "For the VQE, the integration of Qiskit Runtime in your existing code is a piece of cake. There is a (almost) drop-in replacement, called `VQEClient` for the `VQE` class.\n",
-    "\n",
-    "Let's see how you can leverage the runtime on a simple chemistry example: Finding the ground state energy of the lithium hydrate (LiH) molecule at a given bond distance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Problem specification: LiH molecule\n",
-    "\n",
-    "First, we specify the molecule whose ground state energy we seek. Here, we look at LiH with a bond distance of 2.5 Å."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_nature.units import DistanceUnit\n",
-    "from qiskit_nature.second_q.drivers import PySCFDriver\n",
-    "from qiskit_nature.second_q.mappers import ParityMapper\n",
-    "from qiskit_nature.second_q.properties import ParticleNumber\n",
-    "from qiskit_nature.second_q.transformers import ActiveSpaceTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bond_distance = 2.5  # in Angstrom\n",
-    "\n",
-    "# specify driver\n",
-    "driver = PySCFDriver(\n",
-    "    atom=f\"Li 0 0 0; H 0 0 {bond_distance}\",\n",
-    "    basis=\"sto3g\",\n",
-    "    charge=0,\n",
-    "    spin=0,\n",
-    "    unit=DistanceUnit.ANGSTROM,\n",
-    ")\n",
-    "problem = driver.run()\n",
-    "\n",
-    "# specify active space transformation\n",
-    "active_space_trafo = ActiveSpaceTransformer(\n",
-    "    num_electrons=problem.num_particles, num_spatial_orbitals=3\n",
-    ")\n",
-    "\n",
-    "# transform the electronic structure problem\n",
-    "problem = active_space_trafo.transform(problem)\n",
-    "\n",
-    "# construct the parity mapper with 2-qubit reduction\n",
-    "qubit_mapper = ParityMapper(num_particles=problem.num_particles)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Classical reference solution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a reference solution we can solve this system classically with the `NumPyEigensolver`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_algorithms import NumPyMinimumEigensolver\n",
-    "from qiskit_nature.second_q.algorithms.ground_state_solvers import GroundStateEigensolver\n",
-    "\n",
-    "np_solver = NumPyMinimumEigensolver()\n",
-    "np_groundstate_solver = GroundStateEigensolver(qubit_mapper, np_solver)\n",
-    "\n",
-    "np_result = np_groundstate_solver.solve(problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== GROUND STATE ENERGY ===\n",
-      " \n",
-      "* Electronic ground state energy (Hartree): -8.408630641972\n",
-      "  - computed part:      -8.408630641972\n",
-      "  - ActiveSpaceTransformer extracted energy part: 0.0\n",
-      "~ Nuclear repulsion energy (Hartree): 0.635012653104\n",
-      "> Total ground state energy (Hartree): -7.773617988868\n",
-      " \n",
-      "=== MEASURED OBSERVABLES ===\n",
-      " \n",
-      "  0:  # Particles: 4.000 S: 0.000 S^2: 0.000 M: 0.000\n",
-      " \n",
-      "=== DIPOLE MOMENTS ===\n",
-      " \n",
-      "~ Nuclear dipole moment (a.u.): [0.0  0.0  4.72431531]\n",
-      " \n",
-      "  0: \n",
-      "  * Electronic dipole moment (a.u.): [0.0  0.0  6.45025807]\n",
-      "    - computed part:      [0.0  0.0  6.45025807]\n",
-      "    - ActiveSpaceTransformer extracted energy part: [0.0  0.0  0.0]\n",
-      "  > Dipole moment (a.u.): [0.0  0.0  -1.72594276]  Total: 1.72594276\n",
-      "                 (debye): [0.0  0.0  -4.38690851]  Total: 4.38690851\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "target_energy = np_result.total_energies[0]\n",
-    "print(np_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## VQE\n",
-    "\n",
-    "To run the VQE we need to select a parameterized quantum circuit acting as ansatz and a classical optimizer. Here we'll choose a heuristic, hardware efficient ansatz and the SPSA optimizer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 628.397x264.88 with 1 Axes>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from qiskit.circuit.library import EfficientSU2\n",
-    "\n",
-    "ansatz = EfficientSU2(num_qubits=4, reps=1, entanglement=\"linear\", insert_barriers=True)\n",
-    "ansatz.decompose().draw(\"mpl\", style=\"iqx\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from qiskit.utils import algorithm_globals\n",
-    "\n",
-    "# fix random seeds for reproducibility\n",
-    "\n",
-    "np.random.seed(5)\n",
-    "algorithm_globals.random_seed = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_algorithms.optimizers import SPSA\n",
-    "\n",
-    "optimizer = SPSA(maxiter=100)\n",
-    "\n",
-    "initial_point = np.random.random(ansatz.num_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before executing the VQE in the cloud using Qiskit Runtime, let's execute a local VQE first."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_algorithms import VQE\n",
-    "from qiskit.primitives import Estimator\n",
-    "\n",
-    "estimator = Estimator()\n",
-    "\n",
-    "local_vqe = VQE(\n",
-    "    estimator,\n",
-    "    ansatz,\n",
-    "    optimizer,\n",
-    "    initial_point=initial_point,\n",
-    ")\n",
-    "\n",
-    "local_vqe_groundstate_solver = GroundStateEigensolver(qubit_mapper, local_vqe)\n",
-    "\n",
-    "local_vqe_result = local_vqe_groundstate_solver.solve(problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== GROUND STATE ENERGY ===\n",
-      " \n",
-      "* Electronic ground state energy (Hartree): -8.223473551005\n",
-      "  - computed part:      -8.223473551005\n",
-      "  - ActiveSpaceTransformer extracted energy part: 0.0\n",
-      "~ Nuclear repulsion energy (Hartree): 0.635012653104\n",
-      "> Total ground state energy (Hartree): -7.588460897901\n",
-      " \n",
-      "=== MEASURED OBSERVABLES ===\n",
-      " \n",
-      "  0:  # Particles: 3.997 S: 0.438 S^2: 0.631 M: 0.001\n",
-      " \n",
-      "=== DIPOLE MOMENTS ===\n",
-      " \n",
-      "~ Nuclear dipole moment (a.u.): [0.0  0.0  4.72431531]\n",
-      " \n",
-      "  0: \n",
-      "  * Electronic dipole moment (a.u.): [0.0  0.0  1.41136787]\n",
-      "    - computed part:      [0.0  0.0  1.41136787]\n",
-      "    - ActiveSpaceTransformer extracted energy part: [0.0  0.0  0.0]\n",
-      "  > Dipole moment (a.u.): [0.0  0.0  3.31294744]  Total: 3.31294744\n",
-      "                 (debye): [0.0  0.0  8.42067168]  Total: 8.42067168\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(local_vqe_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Runtime VQE\n",
-    "\n",
-    "Let's exchange the eigensolver from a local VQE algorithm to a VQE executed using Qiskit Runtime -- simply by exchanging the `VQE` class by the `qiskit_nature.runtime.VQEClient`.\n",
-    "\n",
-    "First, we'll have to load a provider to access Qiskit Runtime. **Note:** You have to replace the next cell with your provider."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit import IBMQ\n",
-    "\n",
-    "IBMQ.load_account()\n",
-    "provider = IBMQ.get_provider(group=\"open\")  # replace by your runtime provider\n",
-    "\n",
-    "backend = provider.get_backend(\"ibmq_qasm_simulator\")  # select a backend that supports the runtime"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can set up the `VQEClient`. In this first release, the optimizer must be provided as a dictionary, in future releases you'll be able to pass the same optimizer object as in the traditional VQE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_nature.runtime import VQEClient\n",
-    "\n",
-    "runtime_vqe = VQEClient(\n",
-    "    ansatz=ansatz,\n",
-    "    optimizer=optimizer,\n",
-    "    initial_point=initial_point,\n",
-    "    provider=provider,\n",
-    "    backend=backend,\n",
-    "    shots=1024,\n",
-    "    measurement_error_mitigation=True,\n",
-    ")  # use a complete measurement fitter for error mitigation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "runtime_vqe_groundstate_solver = GroundStateEigensolver(qubit_mapper, runtime_vqe)\n",
-    "runtime_vqe_result = runtime_vqe_groundstate_solver.solve(problem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== GROUND STATE ENERGY ===\n",
-      " \n",
-      "* Electronic ground state energy (Hartree): -8.172591259069\n",
-      "  - computed part:      -8.172591259069\n",
-      "  - ActiveSpaceTransformer extracted energy part: 0.0\n",
-      "~ Nuclear repulsion energy (Hartree): 0.635012653104\n",
-      "> Total ground state energy (Hartree): -7.537578605965\n",
-      " \n",
-      "=== MEASURED OBSERVABLES ===\n",
-      " \n",
-      "  0:  # Particles: 3.988 S: 0.596 S^2: 0.951 M: 0.002\n",
-      " \n",
-      "=== DIPOLE MOMENTS ===\n",
-      " \n",
-      "~ Nuclear dipole moment (a.u.): [0.0  0.0  4.72431531]\n",
-      " \n",
-      "  0: \n",
-      "  * Electronic dipole moment (a.u.): [0.0  0.0  1.25645131]\n",
-      "    - computed part:      [0.0  0.0  1.25645131]\n",
-      "    - ActiveSpaceTransformer extracted energy part: [0.0  0.0  0.0]\n",
-      "  > Dipole moment (a.u.): [0.0  0.0  3.467864]  Total: 3.467864\n",
-      "                 (debye): [0.0  0.0  8.81443024]  Total: 8.81443024\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(runtime_vqe_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are interested in the development of the energy, the `VQEClient` allows access to the history of the optimizer, which contains the loss per iteration (along with the parameters and a timestamp). Note that this only holds for the SPSA and QN-SPSA optimizers. Otherwise you have to use a callback to the `VQEClient`, similar to the normal `VQE`.\n",
-    "\n",
-    "We can access this data via the `raw_result` attribute of the ground state solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "runtime_result = runtime_vqe_result.raw_result\n",
-    "history = runtime_result.optimizer_history\n",
-    "loss = history[\"energy\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rcParams[\"font.size\"] = 14\n",
-    "\n",
-    "# plot loss and reference value\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.plot(loss + runtime_vqe_result.nuclear_repulsion_energy, label=\"Runtime VQE\")\n",
-    "plt.axhline(y=target_energy + 0.2, color=\"tab:red\", ls=\":\", label=\"Target + 200mH\")\n",
-    "plt.axhline(y=target_energy, color=\"tab:red\", ls=\"--\", label=\"Target\")\n",
-    "\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.xlabel(\"Iteration\")\n",
-    "plt.ylabel(\"Energy [H]\")\n",
-    "plt.title(\"VQE energy\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0.dev0+f339f71</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.2</td></tr><tr><td><code>qiskit-nature</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>GCC 12.2.1 20220819 (Red Hat 12.2.1-2)</td></tr><tr><td>Python build</td><td>main, Oct 12 2022 00:00:00</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>62.499855041503906</td></tr><tr><td colspan='2'>Wed Dec 07 11:09:57 2022 CET</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import qiskit.tools.jupyter\n",
-    "\n",
-    "%qiskit_version_table\n",
-    "%qiskit_copyright"
-   ]
-  }
- ],
- "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": "3.9.16"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "d4bd6456a0b47d0dd87b9ae107645ad9bf4ec16d9647d052356a2dc29fff80e1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/08_qcschema.ipynb b/docs/tutorials/08_qcschema.ipynb
index 23f260f2a..2a19d4434 100644
--- a/docs/tutorials/08_qcschema.ipynb
+++ b/docs/tutorials/08_qcschema.ipynb
@@ -196,7 +196,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/09_properties.ipynb b/docs/tutorials/09_properties.ipynb
index 9b583ac8a..811e8212d 100644
--- a/docs/tutorials/09_properties.ipynb
+++ b/docs/tutorials/09_properties.ipynb
@@ -198,7 +198,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/10_lattice_models.ipynb b/docs/tutorials/10_lattice_models.ipynb
index 1d9f42662..21646cca4 100644
--- a/docs/tutorials/10_lattice_models.ipynb
+++ b/docs/tutorials/10_lattice_models.ipynb
@@ -1161,7 +1161,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/11_quadratic_hamiltonian_and_slater_determinants.ipynb b/docs/tutorials/11_quadratic_hamiltonian_and_slater_determinants.ipynb
index 42b7ed452..68ac3eb6b 100644
--- a/docs/tutorials/11_quadratic_hamiltonian_and_slater_determinants.ipynb
+++ b/docs/tutorials/11_quadratic_hamiltonian_and_slater_determinants.ipynb
@@ -477,7 +477,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
diff --git a/docs/tutorials/12_deuteron_binding_energy.ipynb b/docs/tutorials/12_deuteron_binding_energy.ipynb
index 533df529e..0e1c96453 100644
--- a/docs/tutorials/12_deuteron_binding_energy.ipynb
+++ b/docs/tutorials/12_deuteron_binding_energy.ipynb
@@ -41,10 +41,10 @@
     "from qiskit_algorithms import VQE\n",
     "from qiskit_algorithms.observables_evaluator import estimate_observables\n",
     "from qiskit_algorithms.optimizers import COBYLA, SLSQP\n",
+    "from qiskit_algorithms.utils import algorithm_globals\n",
     "from qiskit.circuit import QuantumCircuit, Parameter\n",
     "from qiskit.circuit.library import TwoLocal\n",
     "from qiskit.quantum_info import Pauli, SparsePauliOp\n",
-    "from qiskit.utils import algorithm_globals\n",
     "from qiskit_nature.second_q.operators import FermionicOp\n",
     "from qiskit_nature.second_q.mappers import JordanWignerMapper"
    ]
@@ -781,7 +781,7 @@
     "            to know the expectation values.\n",
     "\n",
     "            angles (list): A list with the values that will be used in the\n",
-    "            'bind_parameters' method.\n",
+    "            'assign_parameters' method.\n",
     "\n",
     "        Returns:\n",
     "            list_exp_values (list): A list containing the expectation values\n",
@@ -791,7 +791,7 @@
     "    for observable in observables:\n",
     "        exp_values = []\n",
     "        for angle in angles:\n",
-    "            qc = quantum_circuit.bind_parameters({theta: angle})\n",
+    "            qc = quantum_circuit.assign_parameters({theta: angle})\n",
     "            result = estimate_observables(\n",
     "                Estimator(),\n",
     "                quantum_state=qc,\n",
@@ -909,7 +909,7 @@
    "id": "738bb6e0",
    "metadata": {},
    "source": [
-    "# Acknowledgment\n",
+    "## Acknowledgment\n",
     "\n",
     "I would like to thank Steve Wood, Soham Pal and Siddhartha Morales for the debates in our meetings during the Qiskit Advocate Mentorship Program 2021 - Fall, they were very important for the construction of this tutorial. "
    ]
@@ -919,7 +919,7 @@
    "id": "711a7f4c",
    "metadata": {},
    "source": [
-    "# References\n",
+    "## References\n",
     "\n",
     "[1] Dumitrescu, Eugene F., et al. \"Cloud quantum computing of an atomic nucleus.\" Physical review letters 120.21 (2018): 210501. Arxiv version: https://arxiv.org/pdf/1801.03897.pdf\n",
     "\n",
@@ -962,7 +962,7 @@
     }
    ],
    "source": [
-    "import qiskit.tools.jupyter\n",
+    "import tutorial_magics\n",
     "\n",
     "%qiskit_version_table\n",
     "%qiskit_copyright"
@@ -971,7 +971,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -985,7 +985,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.7"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/tutorial_magics.py b/docs/tutorials/tutorial_magics.py
new file mode 100644
index 000000000..22d88bdd6
--- /dev/null
+++ b/docs/tutorials/tutorial_magics.py
@@ -0,0 +1,96 @@
+# This code is part of a Qiskit project
+#
+# (C) Copyright IBM 2017, 2024
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+# pylint: disable=unused-argument
+
+"""A module for version and copyright magics."""
+
+import datetime
+import platform
+import time
+from sys import modules
+
+from IPython import get_ipython
+from IPython.core.magic import line_magic, Magics, magics_class
+from IPython.display import HTML, display
+
+import qiskit
+
+
+@magics_class
+class Copyright(Magics):
+    """A class of status magic functions."""
+
+    @line_magic
+    def qiskit_copyright(self, line="", cell=None):
+        """A Jupyter magic function return qiskit copyright"""
+        now = datetime.datetime.now()
+
+        html = "<div style='width: 100%; background-color:#d5d9e0;"
+        html += "padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'>"
+        html += "<h3>This code is a part of a Qiskit project</h3>"
+        html += "<p>&copy; Copyright IBM 2017, %s.</p>" % now.year
+        html += "<p>This code is licensed under the Apache License, Version 2.0. You may<br>"
+        html += "obtain a copy of this license in the LICENSE.txt file in the root directory<br> "
+        html += "of this source tree or at http://www.apache.org/licenses/LICENSE-2.0."
+
+        html += "<p>Any modifications or derivative works of this code must retain this<br>"
+        html += "copyright notice, and modified files need to carry a notice indicating<br>"
+        html += "that they have been altered from the originals.</p>"
+        html += "</div>"
+        return display(HTML(html))
+
+
+@magics_class
+class VersionTable(Magics):
+    """A class of status magic functions."""
+
+    @line_magic
+    def qiskit_version_table(self, line="", cell=None):
+        """
+        Print an HTML-formatted table with version numbers for Qiskit and its
+        dependencies. This should make it possible to reproduce the environment
+        and the calculation later on.
+        """
+        html = "<h3>Version Information</h3>"
+        html += "<table>"
+        html += "<tr><th>Software</th><th>Version</th></tr>"
+
+        packages = {"qiskit": qiskit.__version__}
+        qiskit_modules = {module.split(".")[0] for module in modules.keys() if "qiskit" in module}
+
+        for qiskit_module in qiskit_modules:
+            packages[qiskit_module] = getattr(modules[qiskit_module], "__version__", None)
+
+        for name, version in packages.items():
+            if version:
+                html += f"<tr><td><code>{name}</code></td><td>{version}</td></tr>"
+
+        html += "<tr><th colspan='2'>System information</th></tr>"
+
+        sys_info = [
+            ("Python version", platform.python_version()),
+            ("OS", "%s" % platform.system()),
+        ]
+
+        for name, version in sys_info:
+            html += f"<tr><td>{name}</td><td>{version}</td></tr>"
+
+        html += "<tr><td colspan='2'>%s</td></tr>" % time.strftime("%a %b %d %H:%M:%S %Y %Z")
+        html += "</table>"
+
+        return display(HTML(html))
+
+
+_IP = get_ipython()
+if _IP is not None:
+    _IP.register_magics(VersionTable)
+    _IP.register_magics(Copyright)