reverted differentiator tests

lhy0718 · May 3, 2020 · 05bf100 · 05bf100
1 parent 74537b2
commit 05bf100
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Showing 3 changed files with 3 additions and 6 deletions.
diff --git a/tensorflow_quantum/python/differentiators/linear_combination_test.py b/tensorflow_quantum/python/differentiators/linear_combination_test.py
@@ -177,8 +177,7 @@ def test_analytic_functional(self, diff):
     def test_sampled_functional(self, diff):
         """Test that the differentiate_sampled function WORKS."""
         differentiable_op = diff.generate_differentiable_op(
-            sampled_op=circuit_execution_ops.get_sampled_expectation_op(
-                backend=cirq.Simulator()))
+            sampled_op=circuit_execution_ops.get_sampled_expectation_op()
         circuit, names, values, ops, n_samples, true_f, true_g = \
             _simple_op_inputs()
         with tf.GradientTape() as g:

diff --git a/tensorflow_quantum/python/differentiators/parameter_shift_test.py b/tensorflow_quantum/python/differentiators/parameter_shift_test.py
@@ -70,8 +70,7 @@ def test_parameter_shift_sampled(self):
         _simple_op_inputs()
         ps = parameter_shift.ParameterShift()
         op = ps.generate_differentiable_op(
-            sampled_op=circuit_execution_ops.get_sampled_expectation_op(
-                backend=cirq.Simulator()))
+            sampled_op=circuit_execution_ops.get_sampled_expectation_op())
 
         with tf.GradientTape() as g:
             g.watch(values)

diff --git a/tensorflow_quantum/python/differentiators/stochastic_differentiator_test.py b/tensorflow_quantum/python/differentiators/stochastic_differentiator_test.py
@@ -117,8 +117,7 @@ def test_stochastic_differentiator_call_sampled(self, coordinate, generator,
         diff = stochastic_differentiator.SGDifferentiator(
             coordinate, generator, cost, uniform)
         op = diff.generate_differentiable_op(
-            sampled_op=circuit_execution_ops.get_sampled_expectation_op(
-                backend=cirq.Simulator()))
+            sampled_op=circuit_execution_ops.get_sampled_expectation_op())
 
         with tf.GradientTape() as g:
             g.watch(values)