Merge pull request #54 from ChitambarLab/pennylane-update-33

Pennylane update 33

Author: bdoolittle

README.md

@@ -32,7 +32,7 @@ $ pip install qnetvo
 Install PennyLane:
 
 ```
-$ pip install pennylane==0.29
+$ pip install pennylane==0.33
 ```
 
 Import packages:
@@ -48,7 +48,7 @@ Note
 </p>
 <p>
 For optimal use, qNetVO should be used with PennyLane.
-QNetVO is currently compatible with PennyLane v0.29.
+QNetVO is currently compatible with PennyLane v0.33.
 </p>
 </div>
 

environment.yml

@@ -2,7 +2,7 @@ name: qnetvo-dev
 channels:
   - defaults
 dependencies:
-  - python=3.8
+  - python=3.11
   - black
   - sphinx
   - pytest

requirements.txt

@@ -1,4 +1,4 @@
-pennylane~=0.29
+pennylane~=0.33
 qiskit==0.34
 pennylane-qiskit==0.20
 tensorflow>=2.0,<3.0

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = qNetVO
-version = 0.4.3
+version = 0.4.4
 author = Brian Doolittle and Tom Bromley
 author_email = brian.d.doolittle@gmail.com
 description = The Quantum Network Variational Optimizer
@@ -20,7 +20,7 @@ package_dir =
 packages = find:
 python_requires = >=3.8
 install_requires =
-    pennylane~=0.29
+    pennylane~=0.33
 
 [options.packages.find]
 where = src

src/qnetvo/information.py

@@ -97,5 +97,7 @@ def shannon_entropy(probs):
     :rtype: float
     """
     return -(
-        math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])
+        math.sum(
+            [px * math.log2(px) if px != 0 and not (math.isclose(px, 0)) else 0 for px in probs]
+        )
     )

test/cost/mutual_info_test.py

@@ -83,15 +83,26 @@ class TestShannonEntropy:
     def test_shannon_entropy_pure_state(self):
         np.random.seed(123)
 
-        prep_node = [qnet.PrepareNode(1, [0, 1], qnet.ghz_state, 0)]
-        meas_node = [qnet.MeasureNode(1, 4, [0, 1], qml.ArbitraryUnitary, 4**2 - 1)]
+        def test_meas_circ(settings, wires):
+            qml.CNOT(wires=wires[0:2])
+            qml.RZ(settings[0], wires=wires[0])
+            qml.RY(settings[1], wires=wires[0])
+
+        prep_node = [qnet.PrepareNode(wires=[0, 1], ansatz_fn=qnet.ghz_state)]
+        meas_node = [
+            qnet.MeasureNode(num_out=4, wires=[0, 1], ansatz_fn=test_meas_circ, num_settings=2)
+        ]
 
         ansatz = qnet.NetworkAnsatz(prep_node, meas_node)
         shannon_entropy = qnet.shannon_entropy_cost_fn(ansatz)
 
         settings = ansatz.rand_network_settings()
         opt_dict = qnet.gradient_descent(
-            shannon_entropy, settings, step_size=0.08, sample_width=5, num_steps=30
+            shannon_entropy,
+            settings,
+            step_size=0.15,
+            sample_width=5,
+            num_steps=35,
         )
 
         assert np.isclose(opt_dict["scores"][-1], 0, atol=0.0005)