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fpietra merged 5 commits into from
Feb 19, 2026
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docs: apply suggestions from review #4

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1584483
add first batch of suggestions from Junye's review
fpietra Feb 17, 2026
85f7df6
add missing dep
fpietra Feb 17, 2026
f61b3e1
rerun the notebooks
fpietra Feb 18, 2026
05cd303
replace qpy with qasm
fpietra Feb 19, 2026
98911e5
typo
fpietra Feb 19, 2026
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40 changes: 29 additions & 11 deletions docs/guides/algorithmiq-tem.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -160,16 +160,24 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "cc0a8093",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
"\n",
"tem_function_name = \"algorithmiq/tem\"\n",
"\n",
"catalog = QiskitFunctionsCatalog(channel=\"ibm_quantum_platform\")\n",
"# Set IBM Quantum credentials and backend configuration\n",
"personal_token = os.environ.get(\n",
" \"QISKIT_IBM_TOKEN\", \"<API-KEY>\"\n",
") # Replace with your personal token or set the environment variable\n",
"channel = \"ibm_quantum_platform\"\n",
"crn = \"your_crn\" # Replace with the Cloud Resource Name (CRN)\n",
"\n",
"catalog = QiskitFunctionsCatalog(channel=channel, instance=crn, token=personal_token)\n",
"\n",
"# Load your function\n",
"tem = catalog.load(tem_function_name)"
Expand All @@ -188,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"id": "ab05b87f",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -220,6 +228,7 @@
"# Define backend to use. TEM will choose the least-busy device reported by IBM if not specified\n",
"backend_name = \"ibm_marrakesh\"\n",
"\n",
"# Run the TEM function (uses around 3 minutes of QPU time)\n",
"job = tem.run(pubs=[pub], backend_name=backend_name, options=options)"
]
},
Expand All @@ -233,7 +242,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"id": "47b0db71",
"metadata": {},
"outputs": [
Expand All @@ -259,13 +268,22 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"id": "ad5b9a53",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.10229839785546409\n"
]
}
],
"source": [
"result = job.result()\n",
"evs = result[0].data.evs"
"evs = result[0].data.evs\n",
"print(evs[0])"
]
},
{
Expand Down Expand Up @@ -373,15 +391,15 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"id": "0433171c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(<shape=(1,), dtype=float64>), stds=np.ndarray(<shape=(1,), dtype=float64>)), metadata={'evs_non_mitigated': array([0.06979321]), 'stds_non_mitigated': array([0.05510763]), 'evs_mitigated_no_readout_mitigation': array([0.07031416]), 'stds_mitigated_no_readout_mitigation': array([0.05566318]), 'evs_non_mitigated_with_readout_mitigation': array([0.08313754]), 'stds_non_mitigated_with_readout_mitigation': array([0.05702442])})], metadata={'resource_usage': {'RUNNING: OPTIMIZING_FOR_HARDWARE': {'CPU_TIME': 0.966265}, 'RUNNING: WAITING_FOR_QPU': {'CPU_TIME': 11.436938}, 'RUNNING: POST_PROCESSING': {'CPU_TIME': 10.379288999999998}, 'RUNNING: EXECUTING_QPU': {'QPU_TIME': 171.0}}})\n"
"PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(<shape=(1,), dtype=float64>), stds=np.ndarray(<shape=(1,), dtype=float64>)), metadata={'evs_non_mitigated': array([0.09305761]), 'stds_non_mitigated': array([0.05499456]), 'evs_mitigated_no_readout_mitigation': array([0.09371257]), 'stds_mitigated_no_readout_mitigation': array([0.05546237]), 'evs_non_mitigated_with_readout_mitigation': array([0.10156985]), 'stds_non_mitigated_with_readout_mitigation': array([0.05788876])})], metadata={'resource_usage': {'RUNNING: OPTIMIZING_FOR_HARDWARE': {'CPU_TIME': 1.018068}, 'RUNNING: WAITING_FOR_QPU': {'CPU_TIME': 411.145994}, 'RUNNING: POST_PROCESSING': {'CPU_TIME': 10.018932999999997}, 'RUNNING: EXECUTING_QPU': {'QPU_TIME': 187.0}}})\n"
]
}
],
Expand Down Expand Up @@ -423,7 +441,7 @@
"metadata": {
"description": "Introduction to TEM, a Qiskit Function by Algorithmiq, to compute estimations with software post-processing error mitigation using tensor networks.",
"kernelspec": {
"display_name": "Python 3",
"display_name": ".venv",
"language": "python",
"name": "python3"
},
Expand All @@ -437,7 +455,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3"
"version": "3.12.3"
},
"title": "Tensor-network error mitigation (TEM) - A Qiskit Function by Algorithmiq"
},
Expand Down
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