Merge pull request #8 from calculquebec/release

Release onto main

.github/workflows/python-publish.yml

@@ -15,41 +15,32 @@ on:
     types: [published]
   workflow_dispatch:
 
-
 permissions:
   contents: read
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
     permissions:
       id-token: write
     environment: release
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python
-      uses: actions/setup-python@v3
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Install pypa/build
-      run: >-
-        python3 -m
-        pip install
-        build
-        --user
-    - name: Build a binary wheel and a source tarball
-      run: python3 -m build
-    - name: Store the distribution packages
-      uses: actions/upload-artifact@v3
-      with:
-        name: python-package-distributions
-        path: dist/
-
-    - name: Publish package distributions to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.x"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build a binary wheel and a source tarball
+        run: python3 -m build
+      - name: Store the distribution packages
+        uses: actions/upload-artifact@v3
+        with:
+          name: python-package-distributions
+          path: dist/
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1

README.md

@@ -2,7 +2,7 @@
 
 The PennyLane-Snowflurry plugin provides a PennyLane device that allows the use of Anyon Systems' Snowflurry quantum computing platform with PennyLane.
 
-[Penylane](https://pennylane.ai/) is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
+[Pennylane](https://pennylane.ai/) is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
 
 [Snowflurry](https://snowflurry.org/) is a quantum computing framework developed in Julia by Anyon Systems and aims to provide access to quantum hardware and simulators.
 
@@ -12,7 +12,7 @@ PennyLane-Snowflurry makes use of dependencies such as PyJulia and PyCall to all
 
 As shown in the diagram below, this plugin is used in Pennylane as a [device](https://pennylane.ai/plugins/) named `snowflurry.qubit`. This device is defined by the class `SnowflurryQubitDevice`. It converts a PennyLane circuit into a Snowflurry circuit, thanks to packages like PyJulia that allow the communication between Python and Julia environments. The Snowflurry circuit can then be used with the available backends, either a simulator or real quantum hardware. The results are then converted back into PennyLane's format and returned to the user.
 
-![interaction_diagram](./doc/interaction_diagram_extended.png)
+![interaction_diagram](https://raw.githubusercontent.com/calculquebec/pennylane-snowflurry/main/doc/interaction_diagram_extended.png)
 
 ## Local installation
 

pennylane_snowflurry/_version.py

@@ -16,4 +16,5 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.1.0.post1"
+
+__version__ = "0.2.0"

pennylane_snowflurry/execution_config.py

@@ -13,11 +13,14 @@
 # limitations under the License.
 """
 Contains the :class:`ExecutionConfig` data class.
+
+This module has not been modified from the original Pennylane source.
+
 """
 from dataclasses import dataclass
 from typing import Optional
 
-from pennylane.interfaces import SUPPORTED_INTERFACES
+from pennylane.workflow import SUPPORTED_INTERFACES
 from pennylane.gradients import SUPPORTED_GRADIENT_KWARGS
 
 SUPPORTED_GRADIENT_METHODS = [
@@ -99,7 +102,10 @@ def __post_init__(self):
         if self.gradient_keyword_arguments is None:
             self.gradient_keyword_arguments = {}
 
-        if any(arg not in SUPPORTED_GRADIENT_KWARGS for arg in self.gradient_keyword_arguments):
+        if any(
+            arg not in SUPPORTED_GRADIENT_KWARGS
+            for arg in self.gradient_keyword_arguments
+        ):
             raise ValueError(
                 f"All gradient_keyword_arguments keys must be in {SUPPORTED_GRADIENT_KWARGS}, got unexpected values: {set(self.gradient_keyword_arguments) - set(SUPPORTED_GRADIENT_KWARGS)}"
             )