Making VQE tests lighter

pyproject.toml

@@ -60,12 +60,11 @@ ply = "^3.11"
 scikit-learn = "^1.2.1"
 dill = "^0.3.6"
 pytest-cov = "^4.0.0"
-pylint = "^2.16.2"
+pylint = "^3.0.3"
 matplotlib = "^3.7.0"
 qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
-tensorflow = { version = "^2.12.0", markers = "sys_platform == 'linux' or sys_platform == 'darwin'" }
-# TODO: the marker is a temporary solution due to the lack of the tensorflow-io 0.32.0's wheels for Windows, this package is one of
-# the tensorflow requirements
+tensorflow = { version = "^2.15.0", markers = "sys_platform == 'linux'" }
+
 [tool.poe.tasks]
 test = "pytest"
 lint = "pylint src --errors-only"
@@ -74,7 +73,6 @@ docs = "make -C doc html"
 docs-clean = "make -C doc clean"
 test-docs = "make -C doc doctest"
 
-
 [tool.poetry.group.cuda11]
 optional = true
 
@@ -83,7 +81,6 @@ cupy-cuda11x = "^12.0.0"
 cuquantum-python-cu11 = "^23.3.0"
 qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
 
-
 [tool.poetry.group.cuda12]
 optional = true
 

src/qibo/models/qgan.py

@@ -235,7 +235,9 @@ def define_cost_gan(
         y_fake = np.ones((samples, 1))
         # evaluate discriminator on fake examples
         disc_output = discriminator(x_fake)
-        loss = tf.keras.losses.binary_crossentropy(y_fake, disc_output)
+        loss = tf.keras.losses.binary_crossentropy(  # pylint: disable=no-member
+            y_fake, disc_output
+        )
         loss = tf.reduce_mean(loss)
         return loss
 
@@ -264,7 +266,9 @@ def generate_real_samples(samples, distribution, real_samples):
                 np.random.uniform(-0.15, 0.15, n), dtype=tf.complex128
             )
 
-        optimizer = tf.optimizers.Adadelta(learning_rate=self.lr)
+        optimizer = tf.optimizers.Adadelta(  # pylint: disable=no-member
+            learning_rate=self.lr
+        )
         # prepare real samples
         s = self.reference
         # manually enumerate epochs

tests/regressions/tsp_layer6_real.out → tests/regressions/tsp_layer2_imag.out

@@ -82,7 +82,7 @@
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 0.000000000000000000e+00
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-4.245052807528133798e-03
+1.034267702652136951e-07
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
@@ -96,7 +96,7 @@
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
--5.791965300232200242e-01
+-1.125407363285722828e-08
 0.000000000000000000e+00
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 0.000000000000000000e+00
@@ -138,7 +138,7 @@
 0.000000000000000000e+00
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--5.499171963820416176e-01
+8.175715411600245706e-04
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@@ -159,7 +159,7 @@
 0.000000000000000000e+00
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-7.071736736785135848e-03
+-5.513468058776938713e-05
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@@ -264,14 +264,14 @@
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-2.041207767216807190e-03
+7.347588697741100523e-03
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
-3.910005916046373486e-01
+-1.097722566296985974e-01
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00

tests/regressions/tsp_layer6_imag.out → tests/regressions/tsp_layer2_real.out

@@ -82,7 +82,7 @@
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
-1.038491867108072564e-02
+1.142305492602130386e-08
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
@@ -96,7 +96,7 @@
 0.000000000000000000e+00
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 0.000000000000000000e+00
-3.314560044007878736e-01
+1.018967776766042943e-07
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00
@@ -138,7 +138,7 @@
 0.000000000000000000e+00
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 0.000000000000000000e+00
-3.147784053399704796e-01
+-7.402466722830055267e-03
 0.000000000000000000e+00
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 0.000000000000000000e+00
@@ -159,7 +159,7 @@
 0.000000000000000000e+00
 0.000000000000000000e+00
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-7.545474328635344563e-03
+-6.089395260702398522e-06
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@@ -264,14 +264,14 @@
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-2.522794376870529653e-03
+8.115104924275230074e-04
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 0.000000000000000000e+00
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 0.000000000000000000e+00
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--7.008930190081533868e-03
+9.939013723965425262e-01
 0.000000000000000000e+00
 0.000000000000000000e+00
 0.000000000000000000e+00