style: Minor PEP8 fixes

Author: tbabej

docs/ising_qaoa.rst

@@ -227,7 +227,7 @@ we set the following biases on \\( \\sigma_{0} \\) and \\( \\sigma_{1}\\):
 
     qvm_connection = api.QVMConnection()
 
-    J = {(0,1,2): -3}
+    J = {(0, 1, 2): -3}
     h = {0: 1, 1: -1}
 
 We can now run the algorithm ten times with step size 2 to collect statistics (this might take a couple of minutes):

grove/tests/ising/test_ising.py

@@ -13,8 +13,8 @@ def test_energy_value():
 
     assert(np.isclose(ener_ising, -9.9))
 
-    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5 , (0, 2, 3): 0.5, (1, 3): 3.1}
-    h = {0: -2.4, 1: 5.2 , 3: -0.3}
+    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5, (0, 2, 3): 0.5, (1, 3): 3.1}
+    h = {0: -2.4, 1: 5.2, 3: -0.3}
     sol = [1, -1, -1, 1]
     ener_ising = energy_value(h, J, sol)
 
@@ -42,7 +42,7 @@ def test_ising_mock():
     with patch("pyquil.api.QVMConnection") as cxn:
         # Mock the response
         cxn.run_and_measure.return_value = [[1, 0, 1, 0]]
-        cxn.expectation.return_value = [0, 0, 0, 0] # dummy
+        cxn.expectation.return_value = [0, 0, 0, 0]  # dummy
 
     # checkerboard with couplings
     J = {(0, 1): 1, (0, 2): 1, (1, 3): 1, (2, 3): 1}
@@ -56,7 +56,7 @@ def test_ising_mock():
     with patch("pyquil.api.QVMConnection") as cxn:
         # Mock the response
         cxn.run_and_measure.return_value = [[1, 0, 1, 0]]
-        cxn.expectation.return_value = [0, 0, 0, 0] # dummy
+        cxn.expectation.return_value = [0, 0, 0, 0]  # dummy
 
     # checkerboard with biases
     J = {}
@@ -70,7 +70,7 @@ def test_ising_mock():
     with patch("pyquil.api.QVMConnection") as cxn:
         # Mock the response
         cxn.run_and_measure.return_value = [[1, 0, 1, 0, 1]]
-        cxn.expectation.return_value = [0, 0, 0, 0, 0] # dummy
+        cxn.expectation.return_value = [0, 0, 0, 0, 0]  # dummy
 
     J = {(0, 4): -1}
     h = {0: 1, 1: -1, 2: 1, 3: -1}
@@ -83,10 +83,10 @@ def test_ising_mock():
     with patch("pyquil.api.QVMConnection") as cxn:
         # Mock the response
         cxn.run_and_measure.return_value = [[0, 1, 1, 0]]
-        cxn.expectation.return_value = [0, 0, 0, 0, 0, 0, 0] # dummy
+        cxn.expectation.return_value = [0, 0, 0, 0, 0, 0, 0]  # dummy
 
-    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5 , (0, 2, 3): 0.5, (1, 3): 3.1}
-    h = {0: -2.4, 1: 5.2 , 3: -0.3}
+    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5, (0, 2, 3): 0.5, (1, 3): 3.1}
+    h = {0: -2.4, 1: 5.2, 3: -0.3}
     p = 1
     most_freq_string_ising, energy_ising, circuit = ising_qaoa(h, J, num_steps=p, vqe_option=None, connection=cxn)
 
@@ -96,7 +96,7 @@ def test_ising_mock():
     with patch("pyquil.api.QVMConnection") as cxn:
         # Mock the response
         cxn.run_and_measure.return_value = [[0, 1, 1, 0]]
-        cxn.expectation.return_value = [0, 0, 0, 0, 0, 0, 0] # dummy
+        cxn.expectation.return_value = [0, 0, 0, 0, 0, 0, 0]  # dummy
 
     swap_mixer = []
     for i in range(4):
@@ -105,8 +105,8 @@ def test_ising_mock():
                 swap_mixer.append(PauliSum([PauliTerm("X", i, 0.5) * PauliTerm("X", j, 1.0)]))
                 swap_mixer.append(PauliSum([PauliTerm("Y", i, 0.5) * PauliTerm("Y", j, 1.0)]))
 
-    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5 , (0, 2, 3): 0.5, (1, 3): 3.1}
-    h = {0: -2.4, 1: 5.2 , 3: -0.3}
+    J = {(0, 1, 2): 1.2, (0, 1, 2, 3): 2.5, (0, 2, 3): 0.5, (1, 3): 3.1}
+    h = {0: -2.4, 1: 5.2, 3: -0.3}
     p = 1
     most_freq_string_ising, energy_ising, circuit = ising_qaoa(h, J, driver_operators=swap_mixer, num_steps=p, vqe_option=None, connection=cxn)