Merge branch 'main' into fix/qr-typos

README.md

@@ -120,7 +120,7 @@ There are several different options you can specify when calling the primitives.
 
 ### Sampler
 
-This is a primitive that takes a list of user circuits as an input and generates an error-mitigated readout of quasi-probabilities. This provides users a way to better evaluate shot results using error mitigation and enables them to more efficiently evaluate the possibility of multiple relevant data points in the context of destructive interference.
+This is a primitive that takes a list of user circuits as an input and generates an error-mitigated readout of quasi-probability distribution. This provides users a way to better evaluate shot results using error mitigation and enables them to more efficiently evaluate the possibility of multiple relevant data points in the context of destructive interference.
 
 To invoke the `Sampler` primitive within a session:
 

docs/primitives.rst

@@ -5,7 +5,7 @@ With Qiskit Runtime, we are introducing a new set of interfaces, in the form of
 
 The existing Qiskit interface to backends (`backend.run()`) was originally designed to accept a list of circuits and return counts for every job. Over time, it became clear that users have diverse purposes for quantum computing, and therefore the ways in which they define the requirements for their computing jobs are expanding. Therefore, their results also look different.
 
-For example, an algorithm researcher and developer cares about information beyond counts; they are more focused on efficiently calculating quasiprobabilities and expectation values of observables.
+For example, an algorithm researcher and developer cares about information beyond counts; they are more focused on efficiently calculating quasi-probability distribution and expectation values of observables.
 
 Our primitives provide methods that make it easier to build modular algorithms and other higher-order programs. Rather than simply returning counts, they return more immediately meaningful information. Additionally, they provide a seamless way to access the latest optimizations in IBM Quantum hardware and software.
 
@@ -40,18 +40,20 @@ The following primitives are available:
 |                       | near-term quantum     |                                    |
 |                       | algorithms.           |                                    |
 +-----------------------+-----------------------+------------------------------------+
-| Sampler               | Generates             |                                    |
-|                       | quasiprobability      |                                    |
-|                       | distributions from    |                                    |
-|                       | input circuits.       |                                    |
-|                       | This enables the      |                                    |
-|                       | return of error       |                                    |
-|                       | mitigated samples,    |                                    |
-|                       | enhancing the output  |                                    |
-|                       | for sampling          |                                    |
-|                       | algorithms            |                                    |
-|                       | such as amplitude     |                                    |
-|                       | amplification.        |                                    |
+| Sampler               | Allows a user to      | .. image:: images/sampler.png      |
+|                       | input a circuit and   |                                    |
+|                       | then generate         |                                    |
+|                       | quasi-probability     |                                    |
+|                       | distribution.         |                                    |
+|                       | This generation       |                                    |
+|                       | enables users to more |                                    |
+|                       | efficiently evaluate  |                                    |
+|                       | the possibility of    |                                    |
+|                       | multiple relevant     |                                    |
+|                       | data points in the    |                                    |
+|                       | context of            |                                    |
+|                       | destructive           |                                    |
+|                       | interference.         |                                    |
 +-----------------------+-----------------------+------------------------------------+
 
 

docs/tutorials/how-to-getting-started-with-estimator.ipynb

@@ -63,7 +63,7 @@
    "source": [
     "The initial release of Qiskit Runtime includes two primitives:\n",
     "\n",
-    "**Sampler:** Generates quasi-probabilities from input circuits.\n",
+    "**Sampler:** Generates quasi-probability distribution from input circuits.\n",
     "\n",
     "**Estimator:** Calculates expectation values from input circuits and observables."
    ]
@@ -1221,7 +1221,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {2: 0.50125, 0: 0.49875}\n",
+      ">>> Quasi-probability distribution from the sampler job: {2: 0.50125, 0: 0.49875}\n",
       ">>> Expectation value from the estimator job: 0.8525\n"
      ]
     }
@@ -1234,7 +1234,9 @@
     "    estimator = Estimator()\n",
     "\n",
     "    result = sampler.run(sampler_circuit).result()\n",
-    "    print(f\">>> Quasi Distribution from the sampler job: {result.quasi_dists[0]}\")\n",
+    "    print(\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {result.quasi_dists[0]}\"\n",
+    "    )\n",
     "\n",
     "    result = estimator.run(circuit, observable).result()\n",
     "    print(f\">>> Expectation value from the estimator job: {result.values[0]}\")"
@@ -1266,7 +1268,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {2: 0.49025, 0: 0.50975}\n",
+      ">>> Quasi-probability distribution from the sampler job: {2: 0.49025, 0: 0.50975}\n",
       ">>> Expectation value from the estimator job: 0.8475\n"
      ]
     }
@@ -1282,7 +1284,7 @@
     "    estimator_job = estimator.run(circuit, observable)\n",
     "\n",
     "    print(\n",
-    "        f\">>> Quasi Distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
     "    )\n",
     "    print(\n",
     "        f\">>> Expectation value from the estimator job: {estimator_job.result().values[0]}\"\n",
@@ -1327,7 +1329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {0: 0.49575, 2: 0.50425}\n",
+      ">>> Quasi-probability distribution from the sampler job: {0: 0.49575, 2: 0.50425}\n",
       ">>> Expectation value from the estimator job: 0.8315\n"
      ]
     }
@@ -1362,7 +1364,7 @@
     "\n",
     "    # 7. Get results\n",
     "    print(\n",
-    "        f\">>> Quasi Distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
     "    )\n",
     "    print(\n",
     "        f\">>> Expectation value from the estimator job: {estimator_job.result().values[0]}\"\n",

docs/tutorials/how-to-getting-started-with-sampler.ipynb

@@ -63,7 +63,7 @@
    "source": [
     "The initial release of Qiskit Runtime includes two primitives:\n",
     "\n",
-    "**Sampler:** Generates quasi-probabilities from input circuits.\n",
+    "**Sampler:** Generates quasi-probability distribution from input circuits.\n",
     "\n",
     "**Estimator:** Calculates expectation values from input circuits and observables."
    ]
@@ -252,6 +252,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "1ce3cb48",
    "metadata": {
@@ -260,7 +261,7 @@
     }
    },
    "source": [
-    "The `result()` method of the job will return the `SamplerResult`, which includes both the quasi-distribution and job metadata. "
+    "The `result()` method of the job will return the `SamplerResult`, which includes both the quasi-probability distribution and job metadata. "
    ]
   },
   {
@@ -278,14 +279,14 @@
      "output_type": "stream",
      "text": [
       ">>> SamplerResult(quasi_dists=[{0: 0.4999999999999999, 1: 0.0, 2: 0.4999999999999998, 3: 0.0}], metadata=[{}])\n",
-      "  > Quasi-distribution: {0: 0.4999999999999999, 1: 0.0, 2: 0.4999999999999998, 3: 0.0}\n"
+      "  > Quasi-probability distribution: {0: 0.4999999999999999, 1: 0.0, 2: 0.4999999999999998, 3: 0.0}\n"
      ]
     }
    ],
    "source": [
     "result = job.result()\n",
     "print(f\">>> {result}\")\n",
-    "print(f\"  > Quasi-distribution: {result.quasi_dists[0]}\")"
+    "print(f\"  > Quasi-probability distribution: {result.quasi_dists[0]}\")"
    ]
   },
   {
@@ -324,7 +325,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi-distribution: {0: 0.9999999999999991, 1: 6.580329297619248e-33, 2: 0.0, 3: 0.0}\n"
+      ">>> Quasi-probability distribution: {0: 0.9999999999999991, 1: 6.580329297619248e-33, 2: 0.0, 3: 0.0}\n"
      ]
     }
    ],
@@ -335,7 +336,7 @@
     "result = job.result()\n",
     "\n",
     "display(circuit.draw(\"mpl\"))\n",
-    "print(f\">>> Quasi-distribution: {result.quasi_dists[0]}\")"
+    "print(f\">>> Quasi-probability distribution: {result.quasi_dists[0]}\")"
    ]
   },
   {
@@ -384,7 +385,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi-distribution: [{0: 0.4999999999999999, 1: 0.0, 2: 0.4999999999999998, 3: 0.0}, {0: 0.9999999999999991, 1: 6.580329297619248e-33, 2: 0.0, 3: 0.0}]\n"
+      ">>> Quasi-probability distribution: [{0: 0.4999999999999999, 1: 0.0, 2: 0.4999999999999998, 3: 0.0}, {0: 0.9999999999999991, 1: 6.580329297619248e-33, 2: 0.0, 3: 0.0}]\n"
      ]
     }
    ],
@@ -398,7 +399,7 @@
     "result = job.result()\n",
     "\n",
     "[display(cir.draw(\"mpl\")) for cir in circuits]\n",
-    "print(f\">>> Quasi-distribution: {result.quasi_dists}\")"
+    "print(f\">>> Quasi-probability distribution: {result.quasi_dists}\")"
    ]
   },
   {
@@ -438,7 +439,7 @@
      "output_type": "stream",
      "text": [
       ">>> Parameter values: [0, 1, 2, 3, 4, 5]\n",
-      ">>> Quasi-distribution: {0: 0.17158451004815306, 1: 0.0041370682135240654, 2: 0.20402129418492707, 3: 0.6202571275533961}\n"
+      ">>> Quasi-probability distribution: {0: 0.17158451004815306, 1: 0.0041370682135240654, 2: 0.20402129418492707, 3: 0.6202571275533961}\n"
      ]
     }
    ],
@@ -454,7 +455,7 @@
     "\n",
     "display(circuit.draw(\"mpl\"))\n",
     "print(f\">>> Parameter values: {parameter_values}\")\n",
-    "print(f\">>> Quasi-distribution: {result.quasi_dists[0]}\")"
+    "print(f\">>> Quasi-probability distribution: {result.quasi_dists[0]}\")"
    ]
   },
   {
@@ -683,15 +684,15 @@
      "output_type": "stream",
      "text": [
       ">>> SamplerResult(quasi_dists=[{2: 0.49275, 0: 0.50725}], metadata=[{'header_metadata': {}, 'shots': 4000}])\n",
-      "  > Quasi-distribution: {2: 0.49275, 0: 0.50725}\n",
+      "  > Quasi-probability distribution: {2: 0.49275, 0: 0.50725}\n",
       "  > Metadata: {'header_metadata': {}, 'shots': 4000}\n"
      ]
     }
    ],
    "source": [
     "result = job.result()\n",
     "print(f\">>> {result}\")\n",
-    "print(f\"  > Quasi-distribution: {result.quasi_dists[0]}\")\n",
+    "print(f\"  > Quasi-probability distribution: {result.quasi_dists[0]}\")\n",
     "print(f\"  > Metadata: {result.metadata[0]}\")"
    ]
   },
@@ -924,15 +925,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi-distribution: {0: 0.50175, 2: 0.49825}\n",
+      ">>> Quasi-probability distribution: {0: 0.50175, 2: 0.49825}\n",
       ">>> Metadata: {'header_metadata': {}, 'shots': 4000, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.04642554186284542}\n"
      ]
     }
    ],
    "source": [
     "sampler = Sampler(backend=backend, options=options)\n",
     "result = sampler.run(circuit).result()\n",
-    "print(f\">>> Quasi-distribution: {result.quasi_dists[0]}\")\n",
+    "print(f\">>> Quasi-probability distribution: {result.quasi_dists[0]}\")\n",
     "print(f\">>> Metadata: {result.metadata[0]}\")"
    ]
   },
@@ -1041,8 +1042,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi-distribution from the first run: {0: 0.498, 2: 0.502}\n",
-      ">>> Quasi-distribution from the second run: {0: 0.498, 2: 0.502}\n"
+      ">>> Quasi-probability distribution from the first run: {0: 0.498, 2: 0.502}\n",
+      ">>> Quasi-probability distribution from the second run: {0: 0.498, 2: 0.502}\n"
      ]
     }
    ],
@@ -1053,10 +1054,14 @@
     "    sampler = Sampler()\n",
     "\n",
     "    result = sampler.run(circuit).result()\n",
-    "    print(f\">>> Quasi-distribution from the first run: {result.quasi_dists[0]}\")\n",
+    "    print(\n",
+    "        f\">>> Quasi-probability distribution from the first run: {result.quasi_dists[0]}\"\n",
+    "    )\n",
     "\n",
     "    result = sampler.run(circuit).result()\n",
-    "    print(f\">>> Quasi-distribution from the second run: {result.quasi_dists[0]}\")"
+    "    print(\n",
+    "        f\">>> Quasi-probability distribution from the second run: {result.quasi_dists[0]}\"\n",
+    "    )"
    ]
   },
   {
@@ -1160,7 +1165,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {2: 0.50025, 0: 0.49975}\n",
+      ">>> Quasi-probability distribution from the sampler job: {2: 0.50025, 0: 0.49975}\n",
       ">>> Expectation value from the estimator job: 0.848\n"
      ]
     }
@@ -1173,7 +1178,9 @@
     "    estimator = Estimator()\n",
     "\n",
     "    result = sampler.run(circuit).result()\n",
-    "    print(f\">>> Quasi Distribution from the sampler job: {result.quasi_dists[0]}\")\n",
+    "    print(\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {result.quasi_dists[0]}\"\n",
+    "    )\n",
     "\n",
     "    result = estimator.run(estimator_circuit, observable).result()\n",
     "    print(f\">>> Expectation value from the estimator job: {result.values[0]}\")"
@@ -1205,7 +1212,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {2: 0.508, 0: 0.492}\n",
+      ">>> Quasi-probability distribution from the sampler job: {2: 0.508, 0: 0.492}\n",
       ">>> Expectation value from the estimator job: 0.8495\n"
      ]
     }
@@ -1221,7 +1228,7 @@
     "    estimator_job = estimator.run(estimator_circuit, observable)\n",
     "\n",
     "    print(\n",
-    "        f\">>> Quasi Distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
     "    )\n",
     "    print(\n",
     "        f\">>> Expectation value from the estimator job: {estimator_job.result().values[0]}\"\n",
@@ -1266,7 +1273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ">>> Quasi Distribution from the sampler job: {0: 0.50125, 2: 0.49875}\n",
+      ">>> Quasi-probability distribution from the sampler job: {0: 0.50125, 2: 0.49875}\n",
       ">>> Expectation value from the estimator job: 0.8475\n"
      ]
     }
@@ -1301,7 +1308,7 @@
     "\n",
     "    # 7. Get results\n",
     "    print(\n",
-    "        f\">>> Quasi Distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
+    "        f\">>> Quasi-probability distribution from the sampler job: {sampler_job.result().quasi_dists[0]}\"\n",
     "    )\n",
     "    print(\n",
     "        f\">>> Expectation value from the estimator job: {estimator_job.result().values[0]}\"\n",

qiskit_ibm_runtime/sampler.py

@@ -34,7 +34,7 @@
 class Sampler(BasePrimitive, BaseSampler):
     """Class for interacting with Qiskit Runtime Sampler primitive service.
 
-    Qiskit Runtime Sampler primitive service calculates probabilities or quasi-probabilities
+    Qiskit Runtime Sampler primitive service calculates quasi-probability distribution
     of bitstrings from quantum circuits.
 
     The :meth:`run` method can be used to submit circuits and parameters to the Sampler primitive.

qiskit_ibm_runtime/utils/runner_result.py

@@ -36,13 +36,13 @@ def get_quasiprobabilities(
         """Get quasiprobabilites associated with one or more experiments.
 
         Parameters:
-            experiment: Indices of experiments to grab quasiprobabilities from.
+            experiment: Indices of experiments to grab quasi-probability distribution from.
 
         Returns:
             A single distribution or a list of distributions.
 
         Raises:
-            QiskitError: If experiment result doesn't contain quasiprobabilities.
+            QiskitError: If experiment result doesn't contain quasi-probability distribution.
         """
         if experiment is None:
             exp_keys = range(len(self.results))
@@ -64,7 +64,9 @@ def get_quasiprobabilities(
                 dict_list.append(out)
             else:
                 raise QiskitError(
-                    'No quasiprobabilities for experiment "{}"'.format(repr(key))
+                    'No quasi-probability distribution for experiment "{}"'.format(
+                        repr(key)
+                    )
                 )
 
         # Return first item of dict_list if size is 1