Add a post-selection guide

docs/guides/_toc.json

@@ -600,6 +600,10 @@
             {
               "title": "Configure error suppression",
               "url": "/docs/guides/configure-error-suppression"
+            },
+            {
+              "title": "Use postselection in workloads",
+              "url": "/docs/guides/post-selection"
             }
           ]
         },

docs/guides/directed-execution-model.mdx

@@ -28,7 +28,7 @@ To apply error mitigation to a circuit under the framework, your workflow will t
 
 4. Run the template circuit and samplex with the [Executor](#executor-primitive) primitive, which will generate and execute the circuit variants as instructed. 
 
-5. Post-process execution results. For example, you can apply post-selection, or extrapolate mitigated expectation values from the execution results.
+5. Post-process execution results. For example, you can apply postselection, or extrapolate mitigated expectation values from the execution results.
 
 <span id="samplomatic-tools"></span>
 ## Tools for the directed execution model

docs/guides/function-template-chemistry-workflow.ipynb

@@ -1569,7 +1569,7 @@
     "                bitstring_matrix_full, probs_arr_full, avg_occupancy, num_elec_a, num_elec_b\n",
     "            )\n",
     "\n",
-    "        # Create batches of subsamples. We post-select here to remove configurations\n",
+    "        # Create batches of subsamples. We postselect here to remove configurations\n",
     "        # with incorrect hamming weight during iteration 0, since no config recovery was performed.\n",
     "        batches = postselect_and_subsample(\n",
     "            bs_mat_tmp,\n",

docs/guides/intro-to-patterns.mdx

@@ -42,6 +42,6 @@ Depending on whether you are using the Sampler or Estimator primitive, the outco
 
 ## Post-process results
 
-This final step involves stitching the outputs from the prior step back together to obtain the desired result. This can involve a range of classical data-processing steps such as visualizing results, readout error mitigation techniques, marginalizing quasi-probability distributions to ascertain results on smaller sets of qubits, or post-selection on inherent properties of the problem, such as total spin, parity, or particle conservation by removing unphysical observables.
+This final step involves stitching the outputs from the prior step back together to obtain the desired result. This can involve a range of classical data-processing steps such as visualizing results, readout error mitigation techniques, marginalizing quasi-probability distributions to ascertain results on smaller sets of qubits, or postselection on inherent properties of the problem, such as total spin, parity, or particle conservation by removing unphysical observables.
 
 As the field moves from bespoke circuit construction to utility-scale workflows, the flexibility and ease with which Qiskit patterns allow users to compose the different steps of the pattern opens quantum computing to a wide variety of applications and techniques for easy use by quantum computational scientists.