Merge pull request #55 from ChitambarLab/update-qnetvo-version

updating pennylane version

.github/workflows/build_documentation.yml

@@ -7,10 +7,23 @@ jobs:
   docs:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
-    - uses: ammaraskar/sphinx-action@master
+    - name: Cancel Previous Runs
+      uses: styfle/cancel-workflow-action@0.4.1
       with:
-        docs-folder: "docs/"
+        access_token: ${{ github.token }}
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.11
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install .
+        pip install -r docs/requirements.txt
+    - name: Build Documentation
+      run: |
+        sphinx-build -b html docs/source/ docs/build/html
     - name: Commit documentation changes
       run: |
         git clone https://github.com/ChitambarLab/qNetVO.git --branch gh-pages --single-branch gh-pages

.github/workflows/check_documentation.yml

@@ -7,7 +7,16 @@ jobs:
   docs:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v1
-    - uses: ammaraskar/sphinx-action@master
-      with:
-        docs-folder: "docs/"
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.11
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .
+          pip install -r docs/requirements.txt
+      - name: Build Documentation
+        run: |
+          sphinx-build -b html docs/source/ docs/build/html

.github/workflows/formatting_check.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install dependencies
         run: pip install black

.github/workflows/run_tests.yml

@@ -27,10 +27,10 @@ jobs:
 
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
       - uses: actions/checkout@v2
-      - name: Set up Python 3.8
+      - name: Set up Python 3.11
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.11
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

README.md

@@ -1,4 +1,4 @@
-# qNetVO: The Quantum Network Variational Optimizer
+# qNetVO: Quantum Network Variational Optimizer
 
 *Simulate and optimize quantum communication networks using quantum computers.*
 
@@ -32,7 +32,7 @@ $ pip install qnetvo
 Install PennyLane:
 
 ```
-$ pip install pennylane==0.33
+$ pip install pennylane==0.37
 ```
 
 Import packages:
@@ -48,7 +48,7 @@ Note
 </p>
 <p>
 For optimal use, qNetVO should be used with PennyLane.
-QNetVO is currently compatible with PennyLane v0.33.
+QNetVO is currently compatible with PennyLane v0.37.
 </p>
 </div>
 

docs/requirements.txt

@@ -1,7 +1,7 @@
 .
-sphinx>=3.0
+sphinx
 Pygments>=2.7.4
-m2r2==0.3.3
-sphinxcontrib-email==0.3.5
+sphinx-mdinclude>=0.6
+sphinxcontrib-email==0.3.6
 furo
-pennylane==0.28
+pennylane==0.37

docs/source/conf.py

@@ -39,7 +39,7 @@
     "sphinx.ext.mathjax",
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
-    "m2r2",
+    "sphinx_mdinclude",
     "sphinxcontrib.email",
 ]
 source_suffix = [".rst", ".md"]

environment.yml

@@ -3,14 +3,12 @@ channels:
   - defaults
 dependencies:
   - python=3.11
-  - black
   - sphinx
+  - black
   - pytest
   - notebook
   - matplotlib
   - pip
   - pip:
     - -r requirements.txt
-    - furo
-    - m2r2==0.3
-    - sphinxcontrib-email==0.3.5
+    - -r docs/requirements.txt

requirements.txt

@@ -1,7 +1,7 @@
-pennylane~=0.33
-qiskit==0.34
-pennylane-qiskit==0.20
+pennylane==0.37
+qiskit>=1
+pennylane-qiskit==0.37
 tensorflow>=2.0,<3.0
 dask[delayed]==2022.5
 tensornetwork>=0.3,<0.4
-flaky==3.7
+pytest-rerunfailures

setup.cfg

@@ -18,9 +18,9 @@ classifiers =
 package_dir =
     = src
 packages = find:
-python_requires = >=3.8
+python_requires = >=3.9
 install_requires =
-    pennylane~=0.33
+    pennylane==0.37
 
 [options.packages.find]
 where = src

src/qnetvo/gradient_descent.py

@@ -114,9 +114,11 @@ def gradient_descent(
             if not (isinstance(settings, list)):
                 settings = [settings]
         elif interface == "tf":
-            # opt.minimize updates settings in place
-            tf_cost = lambda: cost(*settings)
-            opt.minimize(tf_cost, settings)
+            with tf.GradientTape() as tape:
+                tf_cost = cost(*settings)
+
+            gradients = tape.gradient(tf_cost, settings)
+            opt.apply_gradients(zip(gradients, settings))
 
         elapsed = time.time() - start
 

src/qnetvo/lazy_dask_import.py

@@ -2,6 +2,7 @@
 Lazily imports dask when needed.
 Throws an error if the user does not have dask[delayed] installed.
 """
+
 try:
     import dask
 

src/qnetvo/lazy_tensorflow_import.py

@@ -2,6 +2,7 @@
 Lazily imports tensorflow when needed.
 Throws an error if the user does not have tensorflow installed.
 """
+
 try:
     import tensorflow
 

test/chsh_gradient_descent_test.py

@@ -1,5 +1,4 @@
 import pytest
-from flaky import flaky
 import pennylane as qml
 from pennylane import numpy as np
 
@@ -81,7 +80,7 @@ def test_chsh_gradient_descent_inhomogeneous_settings(self):
 
         assert np.isclose(opt_dict["opt_score"], 2 * np.sqrt(2), atol=1e-3)
 
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_parallel_chsh_gradient_descent(self):
         prepare_nodes = [
             qnet.PrepareNode(1, [0, 1], self.bell_state_RY, 2),
@@ -108,7 +107,7 @@ def test_parallel_chsh_gradient_descent(self):
 
         assert np.isclose(opt_dict["opt_score"], 2 * np.sqrt(2), atol=1e-3)
 
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_parallel_chsh_natural_gradient_descent(self):
         prepare_nodes = [
             qnet.PrepareNode(1, [0, 1], qnet.ghz_state, 0),

test/cost/nlocal_chain_bell_inequality_test.py

@@ -1,5 +1,4 @@
 import pytest
-from flaky import flaky
 from pennylane import numpy as np
 import pennylane as qml
 
@@ -63,7 +62,7 @@ def test_nlocal_chain_22_cost_fn(self, parallel_flag):
             ),
         ],
     )
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_parallel_nlocal_chain_grad_fn(self, natural_grad, prep_nodes, meas_nodes):
         chain_ansatz = qnet.NetworkAnsatz(prep_nodes, meas_nodes)
 
@@ -81,7 +80,7 @@ def test_parallel_nlocal_chain_grad_fn(self, natural_grad, prep_nodes, meas_node
             assert np.allclose(grad, grad_match)
 
     @pytest.mark.parametrize("parallel_flag", [True, False])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_chain_J22_fn(self, parallel_flag):
         prep_nodes = [
             qnet.PrepareNode(1, [0, 1], qnet.local_RY, 2),
@@ -132,7 +131,7 @@ def test_chain_J22_fn(self, parallel_flag):
         assert np.isclose(4, J22(*settings))
 
     @pytest.mark.parametrize("parallel_flag", [True, False])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_chain_I22_fn(self, parallel_flag):
         prep_nodes = [
             qnet.PrepareNode(1, [0, 1], qnet.local_RY, 2),

test/cost/nlocal_star_bell_inequality_test.py

@@ -1,5 +1,4 @@
 import pytest
-from flaky import flaky
 from pennylane import numpy as np
 import pennylane as qml
 
@@ -39,7 +38,7 @@ def trilocal_star_ry_ansatz(self):
         return qnet.NetworkAnsatz(prep_nodes, meas_nodes)
 
     @pytest.mark.parametrize("parallel_flag, nthreads", [(False, 4), (True, 4), (True, 3)])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_bilocal_star_22_cost(self, parallel_flag, nthreads):
         bilocal_star_ansatz = self.bilocal_star_ry_ansatz()
         bilocal_22_cost = qnet.nlocal_star_22_cost_fn(
@@ -55,7 +54,7 @@ def test_bilocal_star_22_cost(self, parallel_flag, nthreads):
         assert np.isclose(bilocal_22_cost(*ideal_settings), -(np.sqrt(2)))
 
     @pytest.mark.parametrize("parallel_flag, nthreads", [(False, 4), (True, 4), (True, 5)])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_trilocal_star_cost(self, parallel_flag, nthreads):
         trilocal_star_ansatz = self.trilocal_star_ry_ansatz()
         trilocal_22_cost = qnet.nlocal_star_22_cost_fn(
@@ -86,7 +85,7 @@ def test_trilocal_star_cost(self, parallel_flag, nthreads):
         assert np.isclose(trilocal_22_cost(*ideal_settings), -np.sqrt(2))
 
     @pytest.mark.parametrize("parallel_flag, nthreads", [(False, 4), (True, 4), (True, 5)])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_bilocal_star_22_cost_gradient_descent(self, parallel_flag, nthreads):
         bilocal_star_ansatz = self.bilocal_star_ry_ansatz()
 
@@ -99,15 +98,15 @@ def test_bilocal_star_22_cost_gradient_descent(self, parallel_flag, nthreads):
             num_steps=10,
             step_size=2,
             sample_width=10,
-            grad_fn=qnet.parallel_nlocal_star_grad_fn(bilocal_star_ansatz)
-            if parallel_flag
-            else None,
+            grad_fn=(
+                qnet.parallel_nlocal_star_grad_fn(bilocal_star_ansatz) if parallel_flag else None
+            ),
         )
 
         assert np.isclose(opt_dict["opt_score"], np.sqrt(2), atol=0.0001)
 
     @pytest.mark.parametrize("parallel_flag, nthreads", [(False, 4), (True, 4), (True, 5)])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_trilocal_star_22_cost_gradient_descent(self, parallel_flag, nthreads):
         trilocal_star_ansatz = self.trilocal_star_ry_ansatz()
 
@@ -118,15 +117,15 @@ def test_trilocal_star_22_cost_gradient_descent(self, parallel_flag, nthreads):
             num_steps=8,
             step_size=2,
             sample_width=10,
-            grad_fn=qnet.parallel_nlocal_star_grad_fn(trilocal_star_ansatz)
-            if parallel_flag
-            else None,
+            grad_fn=(
+                qnet.parallel_nlocal_star_grad_fn(trilocal_star_ansatz) if parallel_flag else None
+            ),
         )
 
         assert np.isclose(opt_dict["opt_score"], np.sqrt(2), atol=0.0001)
 
     @pytest.mark.parametrize("nthreads", [3, 4])
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_bilocal_star_22_cost_natural_gradient_descent(self, nthreads):
         bilocal_star_ansatz = self.bilocal_star_ry_ansatz()
 

test/network_nodes_test.py

@@ -34,8 +34,8 @@ def circuit(settings, wires, cc_wires):
 
     assert len(tape) == 1
     assert tape.wires.tolist() == [0, 1]
-    assert tape.num_params == 0
-    assert tape.get_parameters() == []
+    assert tape.num_params == 1
+    assert all(tape.get_parameters()[0] == mock_settings)
 
 
 def test_network_node_defaults():
@@ -223,5 +223,5 @@ def circuit(settings, wires, cc_wires):
 
     assert len(tape) == 1
     assert tape.wires.tolist() == [0, 1]
-    assert tape.num_params == 0
-    assert tape.get_parameters() == []
+    assert tape.num_params == 1
+    assert all(tape.get_parameters()[0] == mock_settings)

test/nlocal_chain_gradient_descent_test.py

@@ -1,5 +1,4 @@
 import pytest
-from flaky import flaky
 import pennylane as qml
 from pennylane import numpy as np
 
@@ -42,7 +41,7 @@ def test_bilocal_chain_gradient_descent(self, optimization_args):
 
         assert np.isclose(opt_dict["opt_score"], np.sqrt(2), atol=1e-3)
 
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_bilocal_chain_parallel_gradient_descent(self, bilocal_chain_ansatz, optimization_args):
         parallel_grad = qnet.parallel_nlocal_chain_grad_fn(bilocal_chain_ansatz)
 
@@ -57,7 +56,7 @@ def test_bilocal_chain_parallel_gradient_descent(self, bilocal_chain_ansatz, opt
 
         assert np.isclose(opt_dict["opt_score"], np.sqrt(2), atol=1e-3)
 
-    @flaky(max_runs=5, min_passes=1)
+    @pytest.mark.flaky(5)
     def test_bilocal_chain_natural_gradient_descent(self):
         prep_nodes = [
             qnet.PrepareNode(1, [0, 1], qnet.ghz_state, 0),