update

unitary/alpha/quantum_world.py

@@ -486,7 +486,7 @@ def get_histogram(
                 histogram[idx][cast(int, result[idx])] += 1
         return histogram
 
-    def get_histogram_with_all_objects_as_key(
+    def get_correlated_histogram(
         self, objects: Optional[Sequence[QuantumObject]] = None, count: int = 100
     ) -> Dict[Tuple[int], int]:
         """Creates histogram of the whole quantum world (or `objects` if specified)
@@ -576,8 +576,7 @@ def get_binary_probabilities_from_state_vector(
         if objects is None:
             objects = self.public_objects
 
-        simulator = cirq.Simulator()
-        result = simulator.simulate(self.circuit, initial_state=0)
+        result = cirq.Simulator().simulate(self.circuit, initial_state=0)
         state_vector = result.state_vector()
         # qubit_map gives the mapping from object to its index in (the dirac notation of)
         # the state vector.
@@ -633,7 +632,7 @@ def get_binary_probabilities_from_state_vector(
             # Renormalize.
             final_world_distribution[key] = abs(coefficients[index]) ** 2 / prob_sum
 
-        # TODO(pengfeichen): make the above calculation a seperate function, which calculates
+        # TODO(pengfeichen): make the above calculation a separate function, which calculates
         # the distribution of the whole world (or specified objects) by state vector.
 
         # Calculate the distribution of all objects.

unitary/alpha/quantum_world_test.py

@@ -656,7 +656,7 @@ def test_get_histogram_and_get_probabilities_one_binary_qobject(
     )
     histogram = world.get_histogram()
     assert histogram == [{0: 0, 1: 100}]
-    histogram = world.get_histogram_with_all_objects_as_key()
+    histogram = world.get_correlated_histogram()
     assert histogram == {(1,): 100}
     probs = world.get_probabilities()
     assert probs == [{0: 0.0, 1: 1.0}]
@@ -667,7 +667,7 @@ def test_get_histogram_and_get_probabilities_one_binary_qobject(
     alpha.Flip()(l1)
     histogram = world.get_histogram()
     assert histogram == [{0: 100, 1: 0}]
-    histogram = world.get_histogram_with_all_objects_as_key()
+    histogram = world.get_correlated_histogram()
     assert histogram == {(0,): 100}
     probs = world.get_probabilities()
     assert probs == [{0: 1.0, 1: 0.0}]
@@ -681,7 +681,7 @@ def test_get_histogram_and_get_probabilities_one_binary_qobject(
     assert len(histogram[0]) == 2
     assert histogram[0][0] > 10
     assert histogram[0][1] > 10
-    histogram = world.get_histogram_with_all_objects_as_key()
+    histogram = world.get_correlated_histogram()
     assert len(histogram) == 2
     assert histogram[(0,)] > 10
     assert histogram[(1,)] > 10
@@ -714,7 +714,7 @@ def test_get_histogram_and_get_probabilities_one_trinary_qobject(
     )
     histogram = world.get_histogram()
     assert histogram == [{0: 0, 1: 100, 2: 0}]
-    histogram = world.get_histogram_with_all_objects_as_key()
+    histogram = world.get_correlated_histogram()
     assert histogram == {(1,): 100}
     probs = world.get_probabilities()
     assert probs == [{0: 0.0, 1: 1.0, 2: 0.0}]
@@ -746,15 +746,15 @@ def test_get_histogram_and_get_probabilities_two_qobjects(simulator, compile_to_
     )
     histogram = world.get_histogram()
     assert histogram == [{0: 0, 1: 100}, {0: 0, 1: 100, 2: 0}]
-    histogram = world.get_histogram_with_all_objects_as_key()
+    histogram = world.get_correlated_histogram()
     assert histogram == {(1, 1): 100}
     probs = world.get_probabilities()
     assert probs == [{0: 0.0, 1: 1.0}, {0: 0.0, 1: 1.0, 2: 0.0}]
     bin_probs = world.get_binary_probabilities()
     assert bin_probs == [1.0, 1.0]
     histogram = world.get_histogram(objects=[l2], count=1000)
     assert histogram == [{0: 0, 1: 1000, 2: 0}]
-    histogram = world.get_histogram_with_all_objects_as_key(objects=[l2], count=1000)
+    histogram = world.get_correlated_histogram(objects=[l2], count=1000)
     assert histogram == {(1,): 1000}
     probs = world.get_probabilities(objects=[l2], count=1000)
     assert probs == [{0: 0.0, 1: 1.0, 2: 0.0}]