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Author: abbycross

docs/tutorials/ai-transpiler-introduction.ipynb

@@ -15,9 +15,9 @@
     "\n",
     "The **Qiskit AI-powered transpiler service (QTS)** introduces machine learning-based optimizations in both routing and synthesis passes. These AI modes have been designed to tackle the limitations of traditional transpilation, particularly for large-scale circuits and complex hardware topologies.\n",
     "\n",
-    "As of **July 2025**, the **Transpiler Service** has been migrated to the new IBM Quantum® Platform and is no currently longer available. For the latest updates about the status of the transpiler service, please refer to the [transpiler service documentation](https://quantum.cloud.ibm.com/docs/en/guides/qiskit-transpiler-service). You can still use the AI transpiler locally in almost the same way as standard Qiskit transpilation. Simply replace `generate_preset_pass_manager()` with `generate_ai_pass_manager()`. This function constructs a pass manager that integrates the AI-powered routing and synthesis passes directly into your local transpilation workflow.\n",
+    "As of **July 2025**, the **Transpiler Service** has been migrated to the new IBM Quantum® Platform and is no longer available. For the latest updates about the status of the Transpiler Service, please refer to the [transpiler service documentation](/docs/guides/qiskit-transpiler-service). You can still use the AI transpiler locally, similar to standard Qiskit transpilation. Simply replace `generate_preset_pass_manager()` with `generate_ai_pass_manager()`. This function constructs a pass manager that integrates the AI-powered routing and synthesis passes directly into your local transpilation workflow.\n",
     "\n",
-    "### Key features of AI Passes\n",
+    "### Key features of AI passes\n",
     "\n",
     "- Routing passes: AI-powered routing can dynamically adjust qubit paths based on the specific circuit and backend, reducing the need for excessive SWAP gates.\n",
     "    - `AIRouting`: Layout selection and circuit routing\n",
@@ -33,7 +33,7 @@
     "\n",
     "This tutorial evaluates the AI modes using both routing and synthesis passes, comparing the results to traditional transpilation to highlight where AI offers performance gains.\n",
     "\n",
-    "For more details on the available AI passes, see the [AI passes documentation](https://quantum.cloud.ibm.com/docs/en/guides/ai-transpiler-passes).\n",
+    "For more details on the available AI passes, see the [AI passes documentation](/docs/guides/ai-transpiler-passes).\n",
     "\n",
     "\n",
     "### Why use AI for quantum circuit transpilation?\n",
@@ -46,7 +46,7 @@
     "![Graph showing AI transpiler performance against Qiskit](/docs/images/tutorials/ai-transpiler-introduction/ai-transpiler-benchmarks.avif)\n",
     "\n",
     "\n",
-    "In benchmarking tests, QTS consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit’s default pass manager strategy, configured with [`generate_preset_passmanager`]. While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, QTS’s AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum® hardware. For more information on these benchmarks, refer to this [blog.](https://www.ibm.com/quantum/blog/qiskit-performance)\n",
+    "In benchmarking tests, the AI transpiler consistently produced shallower, higher-quality circuits compared to the standard Qiskit transpiler. For these tests, we used Qiskit’s default pass manager strategy, configured with [`generate_preset_passmanager`]. While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, AI-powered passes achieved an average 24% reduction in two-qubit gate counts and a 36% reduction in circuit depth for large circuits (100+ qubits) when transpiling to the heavy-hex topology of IBM Quantum hardware. For more information on these benchmarks, refer to this [blog.](https://www.ibm.com/quantum/blog/qiskit-performance)\n",
     "\n",
     "This tutorial explores the key benefits of AI passes and how it compares to traditional methods."
    ]
@@ -234,7 +234,7 @@
     "\n",
     "For this example, we select the least busy operational IBM Quantum backend that is not a simulator and has at least 100 qubits:\n",
     "\n",
-    "**Note:** Since the least-busy backend can change over time, different devices may be selected on different runs. Device-specific properties such as coupling maps which can lead to  differences in the transpiled circuits."
+    "**Note:** Since the least-busy backend can change over time, different devices might be selected for different runs. Device-specific properties, such as coupling maps, can lead to differences in the transpiled circuits."
    ]
   },
   {
@@ -268,7 +268,7 @@
     "### Create AI and traditional pass managers\n",
     "To evaluate the effectiveness of the AI transpiler, we will perform two transpilation runs. First, we will transpile the circuit using the AI transpiler. Then, we will run a comparison by transpiling the same circuit without the AI transpiler, using traditional methods. Both transpilation processes will use the same coupling map from the chosen backend and the optimization level set to 3 for a fair comparison.\n",
     "\n",
-    "Both are these methods reflect the standard approach to create `PassManager` instances to transpile circuits in Qiskit."
+    "Both of these methods reflect the standard approach to create `PassManager` instances to transpile circuits in Qiskit."
    ]
   },
   {
@@ -338,7 +338,7 @@
    "source": [
     "In this test, we compare the performance of the AI transpiler and the standard transpilation method on the efficient_su2 circuit. The AI transpiler achieves a noticeably shallower circuit depth while maintaining a similar gate count.\n",
     "\n",
-    "- **Circuit depth:** The AI transpiler produces a circuit with lower 2-qubit depth. This is expected, as the AI passes are trained to optimize depth by learning qubit interaction patterns and exploiting hardware connectivity more effectively than rule-based heuristics.\n",
+    "- **Circuit depth:** The AI transpiler produces a circuit with lower two-qubit depth. This is expected, as the AI passes are trained to optimize depth by learning qubit interaction patterns and exploiting hardware connectivity more effectively than rule-based heuristics.\n",
     "\n",
     "- **Gate count:** The total gate count remains similar between the two methods. This aligns with expectations since the standard SABRE-based transpilation explicitly minimizes swap count, which dominates gate overhead. The AI transpiler instead prioritizes overall depth and may occasionally trade off a few additional gates for a shorter execution path.\n",
     "\n",