diff --git a/.github/workflows/github-ci.yaml b/.github/workflows/github-ci.yaml
new file mode 100644
index 0000000..e042c85
--- /dev/null
+++ b/.github/workflows/github-ci.yaml
@@ -0,0 +1,52 @@
+name: SDK build and test
+
+on:
+  push:
+    branches:
+      - dev
+  pull_request:
+    branches:
+      - dev
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: true
+      max-parallel: 3
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install --upgrade .[qiskit,projectq,dev] coveralls
+
+      - name: Run Tests
+        run: |
+          echo "running unit tests"
+          coverage run --source="./src/quantuminspire" -m unittest discover -s src/tests -t src -v
+          echo "running mypy"
+          MYPYPATH=./src mypy --strict --ignore-missing-imports -p quantuminspire
+          echo "running pylint"
+          pylint quantuminspire || true
+          echo "running examples"
+          python ./docs/examples/example_projectq_entangle.py
+          python ./docs/examples/example_projectq_grover.py
+          python ./docs/examples/example_qiskit_conditional.py
+          python ./docs/examples/example_qiskit_entangle.py
+          echo "running notebooks"
+          jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/example_projectq.ipynb
+          jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/grover_algorithm_qi.ipynb
+          jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/qi-performance-test.ipynb
+        env:
+          API_URL: https://api.quantum-inspire.com
+          QI_TOKEN: ${{ secrets.QI_TOKEN }}
diff --git a/.github/workflows/nightly.yaml b/.github/workflows/nightly.yaml
index 9a3553b..ca8929a 100644
--- a/.github/workflows/nightly.yaml
+++ b/.github/workflows/nightly.yaml
@@ -12,14 +12,14 @@ jobs:
 
     steps:
     - name: Checkout Quantum Inspire SDK
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
     - name: Clone Qiskit-terra
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         repository: qiskit/qiskit-terra
         path: qiskit-terra
     - name: Set up Python 3.8
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: 3.8
     - name: pip install prerequisites
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 7308eb3..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-language: python
-matrix:
-  include:
-    - name: python 3.8
-      python: 3.8
-      if: branch = dev
-      dist: focal
-    - name: python 3.9
-      python: 3.9
-      dist: focal
-    - name: python 3.10
-      python: 3.10.1
-      if: branch = dev
-      dist: focal
-install:
-- pip install --upgrade .[qiskit,projectq,dev] coveralls
-env:
-- API_URL=https://staging.quantum-inspire.com
-script:
-- echo $TRAVIS_PULL_REQUEST
-- echo $TRAVIS_BRANCH
-- echo "running unit tests"
-- coverage run --source="./src/quantuminspire" -m unittest discover -s src/tests -t src -v
-- echo "running mypy"
-- MYPYPATH=./src mypy --strict --ignore-missing-imports -p quantuminspire
-- echo "running pylint"
-- pylint quantuminspire || true
-- if [ $TRAVIS_PULL_REQUEST == "false" ]; then
-    echo "running examples";
-    python ./docs/examples/example_projectq_entangle.py;
-    python ./docs/examples/example_projectq_grover.py;
-    python ./docs/examples/example_qiskit_conditional.py;
-    python ./docs/examples/example_qiskit_entangle.py;
-    echo "running notebooks";
-    jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/example_projectq.ipynb;
-    jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/grover_algorithm_qi.ipynb;
-    jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 docs/examples/qi-performance-test.ipynb;
-    echo "cleaning up";
-    python ./docs/examples/delete_projects.py;
-  fi
-after_success:
-- coveralls
-deploy:
-  provider: pypi
-  user: qinspire
-  password:
-    secure: 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
-  on:
-    tags: true
-    branch: master
-  skip_existing: true
diff --git a/docs/examples/example_projectq.ipynb b/docs/examples/example_projectq.ipynb
index 0638ae8..084548b 100644
--- a/docs/examples/example_projectq.ipynb
+++ b/docs/examples/example_projectq.ipynb
@@ -73,7 +73,7 @@
      "output_type": "stream",
      "text": [
       "Measured 0,0,0,0,0\n",
-      "Probabilities: {'10001': 0.525390625, '00000': 0.474609375}\n",
+      "Probabilities: {'10001': 0.4892578125, '00000': 0.5107421875}\n",
       "version 1.0\n",
       "# cQASM generated by Quantum Inspire <class 'quantuminspire.projectq.backend_qx.QIBackend'> class\n",
       "qubits 5\n",
@@ -132,8 +132,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Measured 1,0,0,0,1\n",
-      "Probabilities: {'10001': 0.501953125, '00000': 0.498046875}\n",
+      "Measured 0,0,0,0,0\n",
+      "Probabilities: {'00000': 0.505859375, '10001': 0.494140625}\n",
       "version 1.0\n",
       "# cQASM generated by Quantum Inspire <class 'quantuminspire.projectq.backend_qx.QIBackend'> class\n",
       "qubits 5\n",
@@ -219,9 +219,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/examples/example_qiskit_conditional.py b/docs/examples/example_qiskit_conditional.py
index 61248e5..513de0c 100644
--- a/docs/examples/example_qiskit_conditional.py
+++ b/docs/examples/example_qiskit_conditional.py
@@ -13,7 +13,8 @@
 """
 import os
 
-from qiskit import BasicAer, execute
+from qiskit import transpile
+from qiskit.providers.basic_provider import BasicProvider
 from qiskit.circuit import QuantumRegister, ClassicalRegister, QuantumCircuit
 
 from quantuminspire.credentials import get_authentication
@@ -40,7 +41,8 @@
 qc.measure(q[1], c1)
 qc.measure(q[2], c2)
 
-qi_job = execute(qc, backend=qi_backend, shots=1024)
+qc = transpile(qc, backend=qi_backend)
+qi_job = qi_backend.run(qc, shots=1024)
 qi_result = qi_job.result()
 histogram = qi_result.get_counts(qc)
 print("\nResult from the remote Quantum Inspire backend:\n")
@@ -48,7 +50,9 @@
 [print('{0}\t{1}'.format(state, counts)) for state, counts in histogram.items()]
 
 print("\nResult from the local Qiskit simulator backend:\n")
-backend = BasicAer.get_backend("qasm_simulator")
-job = execute(qc, backend=backend, shots=1024)
+backend = BasicProvider().get_backend(name="basic_simulator")   # qasm_simulator
+qc = transpile(qc, backend=backend)
+job = backend.run(qc, shots=1024)
+
 result = job.result()
 print(result.get_counts(qc))
diff --git a/docs/examples/example_qiskit_entangle.py b/docs/examples/example_qiskit_entangle.py
index a205786..e57a8ec 100644
--- a/docs/examples/example_qiskit_entangle.py
+++ b/docs/examples/example_qiskit_entangle.py
@@ -14,7 +14,7 @@
 """
 import os
 
-from qiskit import execute
+from qiskit import transpile
 from qiskit.circuit import QuantumRegister, ClassicalRegister, QuantumCircuit
 
 from quantuminspire.credentials import get_authentication
@@ -36,7 +36,8 @@
 circuit.cx(q[0], q[1])
 circuit.measure(q, b)
 
-qi_job = execute(circuit, backend=qi_backend, shots=256)
+circuit = transpile(circuit, backend=qi_backend)
+qi_job = qi_backend.run(circuit, shots=256)
 qi_result = qi_job.result()
 histogram = qi_result.get_counts(circuit)
 print('\nState\tCounts')
diff --git a/docs/examples/grover_algorithm_qi.ipynb b/docs/examples/grover_algorithm_qi.ipynb
index 412ce05..fb9185d 100644
--- a/docs/examples/grover_algorithm_qi.ipynb
+++ b/docs/examples/grover_algorithm_qi.ipynb
@@ -38,9 +38,10 @@
     "import numpy as np\n",
     "import os\n",
     "\n",
+    "from qiskit import transpile, QuantumCircuit, QuantumRegister, ClassicalRegister\n",
+    "from qiskit_aer import Aer\n",
+    "from qiskit.providers.basic_provider import BasicProvider\n",
     "from qiskit.tools.visualization import circuit_drawer, plot_histogram\n",
-    "from qiskit import execute, QuantumCircuit, QuantumRegister, ClassicalRegister\n",
-    "from qiskit import BasicAer\n",
     "from IPython.display import display, Math, Latex\n",
     "\n",
     "from quantuminspire.credentials import get_authentication\n",
@@ -77,6 +78,7 @@
     "    \"\"\"\n",
     "    result = []\n",
     "    epsilon = 1/pow(10, decimal_precision)\n",
+    "    state_vector = np.asarray(state_vector)\n",
     "    bit_length = (len(state_vector) - 1).bit_length()\n",
     "    for index, complex_value in enumerate(state_vector):\n",
     "        has_imag_part = np.round(complex_value.imag, decimal_precision) != 0.0\n",
@@ -115,7 +117,7 @@
     "    display(Math(latex_text))\n",
     "\n",
     "    base_states = 2 ** number_of_qubits\n",
-    "    backend = BasicAer.get_backend(backend_name)\n",
+    "    backend = Aer.get_backend(backend_name)\n",
     "    for base_state in range(base_states):\n",
     "        pre_circuit = QuantumCircuit(q_register)\n",
     "        state = base_state\n",
@@ -126,7 +128,9 @@
     "\n",
     "        input_state = r'\\left\\lvert{0:0{1}b}\\right\\rangle'.format(base_state, number_of_qubits)\n",
     "        circuit_total = pre_circuit.compose(q_circuit)\n",
-    "        job = execute(circuit_total, backend=backend)\n",
+    "\n",
+    "        circuit_total = transpile(circuit_total, backend)\n",
+    "        job = backend.run(circuit_total, shots=10)\n",
     "        output_state = job.result().get_statevector(circuit_total)\n",
     "\n",
     "        latex_text = input_state + r'\\mathrm{transforms\\ to}: ' + format_vector(output_state)\n",
@@ -138,11 +142,19 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\dev\\quantuminspire-qiskit10\\env\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 389.136x284.278 with 1 Axes>"
+       "<Figure size 389.338x284.278 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -383,9 +395,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 723.581x284.278 with 1 Axes>"
+       "<Figure size 723.783x284.278 with 1 Axes>"
       ]
      },
      "execution_count": 7,
@@ -649,7 +661,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -680,23 +692,24 @@
     "\n",
     "input_state(qc, q, n)\n",
     "\n",
-    "backend=BasicAer.get_backend('statevector_simulator')\n",
-    "job = execute(qc, backend=backend, shots=10)\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
+    "qc = transpile(qc, backend)\n",
+    "job = backend.run(qc, shots=10)\n",
     "result = job.result()\n",
     "state_vector = result.get_statevector(qc)\n",
-    "m=display( Math('\\mathrm{state\\ after\\ initialization:\\ }' +format_vector(state_vector)))\n",
+    "m=display( Math(r'\\mathrm{state\\ after\\ initialization:\\ }' + format_vector(state_vector)))\n",
     "\n",
     "# apply T rounds of oracle and inversion about the average\n",
     "print('number of iterations T=%d'% T)\n",
     "for t in range(T):\n",
     "    for i in range(n):\n",
     "        qc.barrier(q[i]) # for better visualization\n",
-    "    qc.i(q[0])\n",
+    "    qc.id(q[0])\n",
     "    # Apply T full iterations\n",
     "    black_box(qc, q)\n",
     "    for i in range(n):\n",
     "        qc.barrier(q[i])\n",
-    "    qc.i(q[0])\n",
+    "    qc.id(q[0])\n",
     "    inversion_about_average(qc, q, n)\n",
     "\n",
     "# Measure the output register in the computational basis\n",
@@ -704,8 +717,9 @@
     "    qc.measure(q[j], ans[j])\n",
     "\n",
     "# Execute circuit\n",
-    "backend=BasicAer.get_backend('qasm_simulator')\n",
-    "job  = execute(qc, backend=backend, shots=10)\n",
+    "backend = BasicProvider().get_backend(name=\"basic_simulator\")\n",
+    "qc = transpile(qc, backend=backend)\n",
+    "job = backend.run(qc, shots=10)\n",
     "result = job.result()\n",
     "\n",
     "# Get counts and plot histogram\n",
@@ -727,11 +741,19 @@
    "execution_count": 12,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\dev\\quantuminspire-qiskit10\\env\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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lGjduXJPL1NfX69prr1V+fr5GjRqlffv2aePGjcrKytKWLVvUpUsXvfzyy5o7d66FkSMYFBQU6OGHH1ZBQQB7nhZxu43x8719Iu13Y6WZPzP+NGvhpsA8/YjmKT1mDP/oDV+Ov9tjzNvE8B/BZ0eh94VvX9rAkUpp4Ubv9gNrfLrVeKvMG760gW9zjYnP4QxOyoeAcJdTJH3m5UNWvnwPlFVJC7zMO2GNz7K9f6PQlzawo1BawzDxQcfjkd750hhpwhu+tIEl3/LAZTCqqpXeWufdOr4cf8nYDw9cwinoEzkPhbXTWLhwoebPn69p06ZpyJAhTS63ePFibdu2TbGxsXrttdfUoUOHE5/1799fzz33nCRp5syZqq939p3i3KzP9PQvXPpq0RNNLvP0L1z64ImmC5UITit3+DYkS1K8lNLC+NOs6jpp3pfe7wuB9e4G7ztQvhx/yXhjZVmWd+sgsGrqvO9ASb63gTW7/Dt/F5qvoMS3J4d9bQMLNkjlDAMFAEGj3m08/OTtS0S+fg98tUfKyvNyZwio4jJpsQ9Dv/naBhZuZL61YLNpr/EAlLd8aQNuj9H/CMQ0FPDdR5u8Py99vQYcqZAWbfZuHQCwiuMLa8XFxZoxY4Z69OihuLg4de7cWffcc48qKip08803y+Vy6dlnnz1lvbKyMk2bNk2dOnXSww8/fNp9rFq1SpI0dOhQZWRknPL5uHHjlJCQoPz8fK1cudIv/y7ASm639eNbby9w1tNprpP++53sdRr91kylPv0rZf5jum0xeaOozJhfz0ortgdmzH67hHob2LwvsEP+NGa5g4qroX78JWNuRStvbFTWGHNvOoUT2gCa53gbqK6r1e2fzNXZc+9Rq6dvUp+XfqfnNn5ia2yAGd/mGjmhlZaSCwSVz7cHdvi/H6uuc9Y0AaHeBjwe6x9+zDvirOEAQ70NlFdZn5+v283QwP5EPgr4j6MLa5s3b1b//v31+OOPq7CwUH369FFtba2eeeYZXX/99dq2zagUDBo06JR177//fuXm5uqpp55SUlLSafdzfGzUtLS0Jpc5/tmaNWt8/NcA9snKt+dJwdUh0ImKi4qRJFXWNv4qV0WtkQHGf7+cJKXGJeiOwT/VQ6MmBT5AP7GjQ1tRbX0xzxfh0gZW77B+n9vyg39Y2HA5/pXV9gzNuGan8XBHMAuXNoCmedsG6tz1ap+QrEXX3a/i6S/qzfHT9di69/ROtg+vBQMWWmVDLrD7oPHGdDALl++B6jrpy++s3+/aXcE/RHy4tIG9h6RcL4cE9wc7+iHeCpc28OV31hbXJeNhWzuuPaGGfBSwnmMLa8XFxRo/frwKCwv1+9//XgUFBdq4caMKCws1e/ZsLVq0SOvXr5fL5dKAAQMarLt27Vo9//zzuvzyyzVx4sQz7islJUWSlJfX9DgVxz/bvn277/8ohJyEhASdf/75SkhIsDuUZllr09j2X+UYw88Fs67JbSVJ2YcbP/+zDxm/z/h+OUm6JKO/ru81Ul2S2gQ+QD9wu6UvbHprxK62541waAMFJUZH2moe2df2zAqH4y9JG/fa8wbp4QppR5APCRoubaA5nJIPNcXbNpAQE6c/j5qkHqkdFOGK0KB2GRrXfYjW5NFPQPAqLrNviOZgf3s5XL4HtuyzZ66jsirjQc9gFi5tYJ1NfbNv86SyY/bs26xwaQN29c9D4b6A3chHg5/T+0ThyLGFtenTpys3N1d33XWXnnjiCSUmJp74bMaMGRo4cKDq6uqUkZHR4I202tpa3XrrrYqNjW10iMjGDBs2TJK0YcMG7d2795TPFy9erIoKY/ysI0dseLzHBnU1lTpWVtzoTzjJyMjQiy++2OgQoaHC4zEmKbdDTb0x11YwG9y+qzontta87LXKL294ftfU1+n5TUvkkkvjejQ9T2OwO1jm/dxq/rLvkDGfRzALhzbwnU3XAMm+649Z4XD8JXuPA20g9DkhHzqd5raB2vo6rcrLVv+2XawIF/CJL3Mt+23ffA8EBXKBptEGAsvjsechP2+EQxuoqLZ+OODjDpYyHOSZkI8GP6f3icJRlN0BBMK2bdv09ttvq02bNnrssccaXWbIkCHasmWLBg4c2OD3s2bN0tatW/XII4+oW7dupvY3YcIEpaenKzc3V5MnT9a8efPUuXNnSdL69et1++23n1i2srLheHrz58/XW2+9pQ0bNqioqEhdunTRtddeq/vvv18tW7b05p8dVNa9+6DWvfug3WHYrr6+XseOHVN8fLwiIyPtDscnhyvsK6pIxjxrGUH8AFdURKT+fslNmvjBkxryz/s0td8YdU9ppwOVR/VO9jplHcrVfedO0NmtOtkdqs/229iJqa2XDhyVOqXaF8OZhEMbsHO+w9wjRmfa5TrzsnYIh+MvSfvtbANBPt9muLSB5nBCPnQ6zW0D9yx9RYnR8fpF3wssjhwwz87vgfwS40GryCB9LDhcvgdszQfJBWxXXWsUN+yy/7DUL92+/Z9JOLQBO78HJOM6cHZHe2MIZuSjwc/pfaJw5MjC2ltvvSW3260pU6Y0WZyKj4+XpAaFte3bt+uRRx5R7969de+995reX2xsrObNm6fLL79c69atU9euXZWZmamqqirt2bNHrVu31jXXXKMFCxY0eHNOkp544gl16dJFjz76qNLT07V582b9+c9/1ooVK/T5558rIiJIew9n0G/Mbco8t/FhNN+bdanF0dgnOztb1113nebPn6++ffvaHY5P8m1+yTIvBF7yvKL7YK34+Uw9sf5Dvb71cx2qKldCdKwGtcvQGyOma2Kv8+wOsVnsfmsw70hwF9Yk57cBO8/DYzVGgb91ED9r4vTjX1MnFdl4I4XvgdDnhHzoTHxtA/cuf03r8ndqyaQ/KSbSkV0zOISdfYLaeuOGfscU+2I4E6d/D7g99vYJyAXsV3DUGKbdLrQB+wXDvSEKa6dHPhrcwqFPFG4cebYsW7ZMkjRmzJgml8nNzZXUsLB2xx13qLq6Wi+88IKio6O92ueIESO0adMmzZo1S5988om+++47paamaurUqXrooYf06KOPSpI6dOjQYL0PP/xQbdv+MMbyRRddpLZt22rKlClatWqVLrzwQq/ikKShQ4eqsLDQq3Uio+P1s4d3er2vpqR0yFSXfpf4bXuN6ZmZqfpa6wfanjp1qullCwoKJBnDgW7atMn0eq+88oqXUQVOl3Ou1fDrn270s9+NlZLiT79+UtwPf878WdPLlR6Tnvz3qb+f9+5C/f7aO01G67v4yGhlTXrU5/WHduyuf131G/8FZELPzEwdqw/8RAdDrn1cXYf/vNHPztQGzB5/qek28If7HtDuta+YC7YZaANNu+wPK5TYtnujn1nRBi4cc6mOFmwzGa3vmtMGnHz8YxNaa/z/bGnyc3+1gaaOf9HhMqWn9zYZre+4BpjnTS4k+ZYP2ZELWd0Gfr/sVS3bt1VLJv1JbVoknXmFRtjVBhB+fnL3YqWmD2j0MytygbFXTtChvV+ZjNZ3oZYLSNZcB850vyDQuUBFVb3S088yGa3vyAWa1r7naF1w8+uNfmbFfYHlK9Zq5pTGH972J9pA0/peNkO9L57e6GdWfA/MfuIp3bjkCZPRBqfmti8z7MhHzXBizurUPlE4cLt/mHNm1KhRXt2zP5kjC2vH5zk766zGE6+6ujqtXr1aUsPC2saNGxUREaFJkyadsk55ebkkac2aNSeKYzt27GgwP1vXrl01Z86cRve5detWSUbR62QnF9WOO75MXl7jE06eSWFhodfrRsW28GlfdsovyFdddeWZF/SzHw/neTpVVVUn/vRmPV+PfSAkd2/6NYWkeCnFZNOJiDC/7Mmqqmss+f/RIjo24Ps4rt7tVq27TrXuenk8UlVdjVxyKTbKu4J+fkGBKmsDP9B4n+/bcWPMtgFfj78klZaV0QaaYFUbqHc3/XyqFW2gqPiQihzUBkLt+LdIqT/t54FvAxFcA5pgVRv4MW9yGsm3fMiOXMjKNvDbpf/U8n1b9en1D6htM25i2NUGEH7qTjPprRW5QPGhw8p30HeBv74HJGuuAzHxiaf9PNBtwBURSS7QBKu+B6LbNv26khX3BWrq6mgDTbCqDWRUNJ3DWfE9UF5RGVT3ynxhZfsyw1/5qBlOzFmd2icKNwcOHPB5XUcW1ioqKiRJx441/jbT22+/reLiYiUmJqpr164NPnO73af9H1pbW3vi85Orm6dTXFystWvXKioqSldcccUZl1++fLkkqXdv357O/vFbcWZERp/h8aIg1KljJ1veWGvRwnwWEBcXd+JPb9ZLS0vzOq5ASWzZdNsoNfG/PynOSJ7cbqm06fpMk9uKiXJZ8v8jPtL7Tqyv3shaqVv+/UMRPumpqTorqY123vaMV9vp1LGjJU/8xEQ1PbnVmdqA2eN/um21bBFHG2iCVW1A7qb3YUUbaJ2apJha57SBUDv+UXGnv5nmrzbQ1Hbc9dVcA5pg2TXgR7zJaSTf8iE7ciGr2sDeo0V6btMnio2MVs+595z4/ai0Xvrwuvu82pZdbQDhJ0JNP2RhRS7QKjlRLgd9F/jre0Cy5jrgijj9raNA5wL1tVXkAk2w6nsgJTGhyc+suC8Q5XLTBppgVRuIj236OmDF90CLuOigulfmCyvb15n4Mx81w4k5q1P7ROHA7XafeIOwffv2Pm/H5fF47BwmOSD69Omjbdu26dlnn9W0adMafFZQUKAhQ4aooKBA559/vlatWmVqmzNnztSf//xnXXTRRfrss8+8iuf222/XnDlzNGXKFL3+euOvzh+Xl5enwYMHa8iQIfr444+92k9zVNdJ973d/O3kZn2mdx8do1E/f1xDrvxDo8s8/QuXMgZdqQl/+KhZ+5p9vXSa7/WAyc7ONr3s1q1bfRo/t1evXr6EFhD7D0l/a+Q1fLNm/sx4IqmkUpr5nvfrXz5Auqy/7/s3y1NTp7o/vRP4HflR1CMT5YoJ/EnwWbb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       "text/plain": [
-       "<Figure size 2247.21x785.944 with 1 Axes>"
+       "<Figure size 2247.42x1204 with 1 Axes>"
       ]
      },
      "execution_count": 12,
@@ -811,7 +833,8 @@
    "outputs": [],
    "source": [
     "qi_backend = QI.get_backend('QX single-node simulator')\n",
-    "j=execute(qc, backend=backend, shots=512)"
+    "qc=transpile(qc, backend=qi_backend)\n",
+    "j=qi_backend.run(qc, shots=512)"
    ]
   },
   {
@@ -831,7 +854,7 @@
      "output_type": "stream",
      "text": [
       "Generated histogram:\n",
-      "{'111': 486, '001': 10, '011': 2, '000': 4, '110': 6, '010': 2, '100': 2}\n"
+      "{'000': 3, '001': 1, '010': 4, '011': 2, '100': 4, '101': 2, '110': 3, '111': 493}\n"
      ]
     }
    ],
@@ -855,7 +878,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -921,7 +944,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "latex_envs": {
    "bibliofile": "biblio.bib",
@@ -942,5 +965,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/docs/examples/qi-performance-test.ipynb b/docs/examples/qi-performance-test.ipynb
index edfb34d..cafd2bf 100644
--- a/docs/examples/qi-performance-test.ipynb
+++ b/docs/examples/qi-performance-test.ipynb
@@ -29,7 +29,7 @@
     "import numpy as np\n",
     "from IPython.display import display\n",
     "\n",
-    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
+    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, transpile\n",
     "from qiskit.tools.visualization import plot_histogram, circuit_drawer\n",
     "\n",
     "from quantuminspire.credentials import get_authentication\n",
@@ -50,9 +50,17 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\dev\\quantuminspire-qiskit10\\env\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1058.03x451.5 with 1 Axes>"
       ]
@@ -126,7 +134,8 @@
    "outputs": [],
    "source": [
     "qi_backend = QI.get_backend('QX single-node simulator')\n",
-    "job = execute(qc, qi_backend)"
+    "qc = transpile(qc, qi_backend)\n",
+    "job = qi_backend.run(qc)"
    ]
   },
   {
@@ -146,7 +155,7 @@
      "output_type": "stream",
      "text": [
       "Generated histogram:\n",
-      "{'0000': 71, '0001': 63, '0010': 69, '0011': 63, '0100': 61, '0101': 60, '0110': 71, '0111': 54, '1000': 62, '1001': 61, '1010': 74, '1011': 57, '1100': 62, '1101': 61, '1110': 65, '1111': 70}\n"
+      "{'0000': 61, '0001': 63, '0010': 65, '0011': 71, '0100': 62, '0101': 60, '0110': 62, '0111': 68, '1000': 64, '1001': 64, '1010': 63, '1011': 67, '1100': 63, '1101': 61, '1110': 67, '1111': 63}\n"
      ]
     }
    ],
@@ -170,7 +179,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -210,7 +219,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "time needed: 5.8 [s]\n"
+      "time needed: 3.8 [s]\n"
      ]
     }
    ],
@@ -218,7 +227,8 @@
     "q, qc = pcircuit(10, 10)\n",
     "start_time = time.time()\n",
     "\n",
-    "job = execute(qc, qi_backend, shots=8)\n",
+    "qc = transpile(qc, qi_backend)\n",
+    "job = qi_backend.run(qc, shots=8)\n",
     "job.result()\n",
     "interval = time.time() - start_time\n",
     "\n",
@@ -250,7 +260,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "latex_envs": {
    "bibliofile": "biblio.bib",
@@ -271,5 +281,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/setup.py b/setup.py
index e8e6ce6..c2456e1 100644
--- a/setup.py
+++ b/setup.py
@@ -56,14 +56,15 @@ def get_long_description():
           'Programming Language :: Python :: 3.9',
           'Programming Language :: Python :: 3.10',
           'Programming Language :: Python :: 3.11',
+          'Programming Language :: Python :: 3.12',
           'License :: OSI Approved :: Apache Software License'],
       license='Apache 2.0',
       packages=['quantuminspire', 'quantuminspire.qiskit', 'quantuminspire.projectq'],
       install_requires=['coverage>=4.5.1', 'matplotlib>=2.1', 'pylatexenc', 'coreapi>=2.3.3', 'numpy>=1.20', 'jupyter',
-                        'nbimporter', 'qilib'],
+                        'nbimporter', 'qilib', 'setuptools'],
       extras_require={
-          'qiskit': ["qiskit>=0.40.0", 'qiskit-aer'],
-          'projectq': ["projectq>=0.4"],
-          'dev': ['pytest>=3.3.1', "pylint", "mypy>=0.670"],
-          'rtd': ['sphinx', 'sphinx_rtd_theme', 'nbsphinx', 'sphinx-automodapi', 'recommonmark'],
+          "qiskit": ["qiskit>=0.46,<1.0", "qiskit-aer"],
+          "projectq": ["projectq>=0.8.0"],
+          "dev": ["pytest>=3.3.1", "pylint", "mypy>=0.670"],
+          "rtd": ["sphinx", "sphinx_rtd_theme", "nbsphinx", "sphinx-automodapi", "recommonmark"],
       })
diff --git a/src/quantuminspire/qiskit/qi_job.py b/src/quantuminspire/qiskit/qi_job.py
index 74e0232..660890b 100644
--- a/src/quantuminspire/qiskit/qi_job.py
+++ b/src/quantuminspire/qiskit/qi_job.py
@@ -35,19 +35,20 @@
 class QIJob(Job):  # type: ignore
     """
     A Qiskit job that is executed on the Quantum Inspire platform. A QIJob is normally created by calling
-    ``execute`` (which triggers a `run` on the QuantumInspireBackend ``qi_backend``).
+    ``run`` on QuantumInspireBackend ``qi_backend``.
 
-    .. code::
+    .. code-block:: python
 
         qc = QuantumCircuit(5, 5)
         qc.h(0)
         qc.cx(0, range(1, 5))
         qc.measure_all()
 
-        job = execute(qc, qi_backend, shots=1024)
+        qc = transpile(qc, qi_backend)
+        job = qi_backend.run(qc, shots=1024)
         result = job.result()
 
-    The return value of ``execute`` is an instance of QIJob (Qiskit job) and is the equivalent to the Quantum
+    The return value of ``run`` is an instance of QIJob (Qiskit job) and is the equivalent to the Quantum
     Inspire project. It is a container to handle one or more (asynchronous) Qiskit circuits or experiments.
     A Qiskit circuit is equivalent to a Quantum Inspire job.
 
@@ -190,7 +191,7 @@ def add_job(self, job: QuantumInspireJob) -> None:
         """ Add a Quantum Inspire job to the list. The list contains the (Quantum Inspire) jobs created for the
         submitted experiments in this particular QIJob.
 
-        :param job: QuatumInspireJob (submitted) that has to be added to the list of jobs created for the experiments
+        :param job: QuantumInspireJob (submitted) that has to be added to the list of jobs created for the experiments
          in QIJob.
         """
         self.jobs.append(job)
@@ -198,13 +199,13 @@ def add_job(self, job: QuantumInspireJob) -> None:
     def queue_position(self, refresh: bool = False) -> Optional[int]:
         """
         Return the position for this Job in the Quantum Inspire queue (when in status QUEUED).
-        Currently we don't have this info available.
+        Currently, we don't have this info available.
 
         :param refresh: If ``True``, re-query the server to get the latest value.
-                Otherwise return the cached value, when available. Not used.
+                Otherwise, return the cached value, when available. Not used.
 
         :return:
-            The queue position of the job. Currently None (not available).
+            The queue position of the job. Currently, None (not available).
         """
         return None
 
diff --git a/src/tests/quantuminspire/test_job.py b/src/tests/quantuminspire/test_job.py
index e2f5d85..e2739b8 100644
--- a/src/tests/quantuminspire/test_job.py
+++ b/src/tests/quantuminspire/test_job.py
@@ -34,7 +34,7 @@ def test_qi_job_invalid_job_identifier(self):
         api.get_job.side_effect = ErrorMessage('TestMock')
         job_identifier = 1
         self.assertRaises(ValueError, QuantumInspireJob, api, job_identifier)
-        api.get_job.called_once()
+        api.get_job.assert_called_once()
 
     def test_check_status(self):
         expected = 'RUNNING'