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58 changes: 54 additions & 4 deletions docs/guides/q-ctrl-performance-management.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "dde95705",
"id": "078f60a7-7919-400a-8b76-1be53dd916ae",
"metadata": {},
"source": [
"# Performance Management: A Qiskit Function by Q-CTRL Fire Opal\n",
Expand All @@ -19,7 +19,34 @@
"* Qiskit Functions are an experimental feature available only to IBM Quantum® Premium Plan users. They are in preview release status and subject to change.\n",
"</Admonition>\n",
"</CloudContent>\n",
"\n",
"<Admonition type=\"note\">\n",
"Qiskit Functions are an experimental feature available only to IBM Quantum&reg; Premium Plan users. They are in preview release status and subject to change.\n",
"</Admonition>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a2d3461-3d26-4244-a58b-9499e098cd10",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"version-info"
]
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "af880e35-4670-41f9-9542-9b13a2cece37",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Overview\n",
"\n",
"Fire Opal Performance Management makes it simple for anyone to achieve meaningful results from quantum computers at scale without needing to be quantum hardware experts. When running circuits with Fire Opal Performance Management, AI-driven error suppression techniques are automatically applied, enabling the scaling of larger problems with more gates and qubits. This approach reduces the number of shots required to reach the correct answer, with no added overhead — resulting in significant savings in both compute time and cost.\n",
Expand Down Expand Up @@ -224,7 +251,7 @@
"\n",
"service = QiskitRuntimeService()\n",
"instance = service.active_account()[\"instance\"]\n",
"backend_name = service.least_busy().name"
"backend_name = \"ibm_torino\""
]
},
{
Expand Down Expand Up @@ -553,7 +580,8 @@
"pub_result = sampler_result[0]\n",
"counts = pub_result.data.c.get_counts()\n",
"\n",
"print(f\"Counts for the meas output register: {counts}\")"
"# Show a few items from the counts dictionary\n",
"list(counts.items())[:10]"
]
},
{
Expand All @@ -566,6 +594,28 @@
"Plot the bitstring with the highest counts to see if the hidden bitstring was the mode."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "799a7331-c029-4d50-8819-c7fe23627d27",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"# This cell is hidden from the user. It checks the top result is the\n",
"# hidden bitstring. This can sometimes happen if the backend is having\n",
"# a bad day.\n",
"most_common_result = max(counts.items(), key=lambda x: x[1])[0][0]\n",
"assert most_common_result == hidden_bitstring"
]
},
{
"cell_type": "code",
"execution_count": 15,
Expand Down