Docs improvements from @jyu00 and rename Latest updates to Changelog

docs/guides/_toc.json

@@ -18,7 +18,7 @@
           "url": "/docs/guides/composer"
         },
         {
-          "title": "Latest updates",
+          "title": "Changelog",
           "url": "/docs/guides/latest-updates"
         }
       ],

docs/guides/calibration-jobs.mdx

@@ -68,6 +68,10 @@ These calibrations are run simultaneously on qubits that require calibration.
 
 Daily benchmarking gives a comprehensive view of quantum computer performance and generates the metrics sent to users through Qiskit. They help users choose qubits, optimize compilations, and better anticipate expected circuit performance. You can view reported numbers either programmatically or on the [Compute resources](https://quantum.cloud.ibm.com/computers) page (click any QPU to open its detailed information card). Find more details about each metric in the [documentation](/docs/guides/qpu-information).
 
+<Admonition type="tip" title="Device drift">
+The impact of device drift (hardware performance that degrades over time) depends on many factors, such as when calibrations were last run, the specific experiment performed, any TLS activities, and so on. If a particular workload is very sensitive to error values of device parameters, you can perform a real-time benchmarking of the device parameters by following [this tutorial](/docs/tutorials/real-time-benchmarking-for-qubit-selection) in IBM Quantum Learning .
+</Admonition>
+
 ### Single-qubit performance
 
 * [Randomized benchmarking](/docs/guides/qpu-information#median-sx-error) (RB) in batched groups  

docs/guides/estimate-job-run-time.ipynb

@@ -82,6 +82,41 @@
     "\n",
     "If you aren't using any advanced error mitigation techniques or custom `rep_delay`, you can use `2+0.00035*<num executions>` as a quick formula.\n",
     "\n",
+    "### Estimate usage locally with Qiskit\n",
+    "\n",
+    "This code example demonstrates how to use Qiskit to calculate circuit time:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "# Schedule the circuit to get more accurate timing\n",
+    "pm = generate_preset_pass_manager(\n",
+    "    target=backend.target,\n",
+    "    optimization_level=0,\n",
+    "    scheduling_method=\"alap\"\n",
+    ")\n",
+    "\n",
+    "scheduled_circuits = pm.run(isa_circuits)\n",
+    "\n",
+    "init_duration = backend.target[\"reset\"][(0,)].duration\n",
+    "rep_delay = sampler.options.execution.rep_delay or backend.default_rep_delay\n",
+    "\n",
+    "circuit_duration = 0\n",
+    "\n",
+    "for circuit in scheduled_circuits:\n",
+    "    # Estimate circuit length\n",
+    "    circuit_duration += circuit.estimate_duration(backend.target)\n",
+    "\n",
+    "    # Add INIT time\n",
+    "    if sampler.options.execution.init_qubits:\n",
+    "        circuit_duration += init_duration\n",
+    "\n",
+    "    # Add rep_delay\n",
+    "    circuit_duration += rep_delay\n",
+    "\n",
+    "total_time = 2 + (circuit_duration*shots)\n",
+    "print(f\"Total estimated usage is {math.ceil(total_time)} seconds\")\n",
+    "```\n",
+    "\n",
     "## Next steps\n",
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",

docs/guides/faq.mdx

@@ -84,4 +84,8 @@ Find instructions on the [Closing an account](https://cloud.ibm.com/docs/account
 
 ## Why do I see a message that my account is inactive when I try to log on?
 
-If you see the message "This account is inactive", visit the [IBM Cloud Docs](https://cloud.ibm.com/docs/account?topic=account-ts_accnt_inactive) to learn why and how to fix it, or access [IBM Cloud](https://cloud.ibm.com) to verify if you have any pending actions to complete in your account.
+If you see the message "This account is inactive", visit the [IBM Cloud Docs](https://cloud.ibm.com/docs/account?topic=account-ts_accnt_inactive) to learn why and how to fix it, or access [IBM Cloud](https://cloud.ibm.com) to verify if you have any pending actions to complete in your account.
+
+## What is the impact of device drift?
+
+The impact of device drift (hardware performance that degrades over time) depends on many factors, such as when calibrations were last run, the specific experiment performed, any TLS activities, and so on. If a particular workload is very sensitive to error values of device parameters, you can perform a real-time benchmarking of the device parameters by following [this tutorial](/docs/tutorials/real-time-benchmarking-for-qubit-selection) in IBM Quantum Learning .

docs/guides/job-limits.mdx

@@ -19,7 +19,7 @@ When you send a job to an IBM® QPU, it is first sent to the job validation s
 <span id="max-shots"></span>
 ## Maximum executions
 
-At most, **10 million** executions are allowed for Sampler jobs (Estimator jobs can be split into smaller sub-jobs, so this limit doesn't apply).  The number of executions is the number of circuits times the number of shots, where the circuits are those generated after PUB elements are broadcasted.
+At most, **10 million** executions are allowed for Sampler jobs due to output size.  The number of executions is the number of circuits times the number of shots, where the circuits are those generated after PUB elements are broadcasted.
 
 For example, if you have a PUB with one circuit and parameters with shape (4, 1), this would render 4 circuits. If you requested 2,000 shots, then the total number of executions is $4 \times 2,000 = 8,000$.
 

docs/guides/latest-updates.mdx

@@ -1,5 +1,5 @@
 ---
-title: Latest updates
+title: Changelog
 description: The latest updates from Qiskit and IBM Quantum, including the latest package release summaries and platform updates.
 in_page_toc_min_heading_level: 2
 in_page_toc_max_heading_level: 2
@@ -8,7 +8,7 @@ in_page_toc_max_heading_level: 2
 
 {/* cspell:ignore GGUF */}
 
-# Latest updates
+# Changelog
 
 *Last updated 8 April 2026*