clean up

.github/workflows/build.yml

@@ -0,0 +1,39 @@
+name: Python package
+
+on:
+  push:
+    branches:
+    - master
+  pull_request:
+    branches:
+    - master
+
+jobs:
+
+  build:
+    name: Build for (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ['ubuntu-latest']
+        python-version: ['3.8']
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Python info
+        shell: bash -l {0}
+        run: |
+          which python3
+          python3 --version
+      - name: Upgrade pip and install dependencies
+        run: |
+          python3 -m pip install --upgrade pip setuptools
+          python3 -m pip install .[dev,publishing]
+      - name: Run unit tests
+        run: pytest -v
+      - name: Verify that we can build the package
+        run: python3 setup.py sdist bdist_wheel

.github/workflows/coverage.yml

@@ -0,0 +1,34 @@
+name: Code coverage
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  coverage:
+    runs-on: ubuntu-latest
+    timeout-minutes: 60
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.9'
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox coveragepy-lcov 'coverage<7'
+      - name: Run coverage
+        run: tox -ecoverage
+      - name: Convert to lcov
+        run: coveragepy-lcov --output_file_path coveralls.info
+      - name: Upload report to Coveralls
+        uses: coverallsapp/github-action@v2
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          file: coveralls.info
+          format: lcov

README.md

@@ -1,6 +1,15 @@
-# HHL linear solvers
-Contains an implementation to solve systems of linear equations using the HHL algorithm.
-Original implementation by [@anedumla](https://github.com/anedumla) : https://github.com/anedumla/quantum_linear_solvers
+![Platform](https://img.shields.io/badge/platform-Linux-blue)
+[![Python](https://img.shields.io/badge/Python-3.8-informational)](https://www.python.org/)
+[![Qiskit](https://img.shields.io/badge/Qiskit-%E2%89%A5%200.44.2-6133BD)](https://github.com/Qiskit/qiskit)
+[![License](https://img.shields.io/github/license/quantumapplicationlab/hhl-prototype?label=License)](https://github.com/quantumapplicationlab/hhl-prototype/blob/main/LICENSE.txt)
+[![Code style: Black](https://img.shields.io/badge/Code%20style-Black-000.svg)](https://github.com/psf/black)
+[![Tests](https://github.com/quantumapplicationlab/hhl-prototype/actions/workflows/coverage.yml/badge.svg)](https://github.com/quantumapplicationlab/hhl-prototype/actions/workflows/coverage.yml)
+[![Coverage Status](https://coveralls.io/repos/github/QuantumApplicationLab/hhl-prototype/badge.svg?branch=main)](https://coveralls.io/github/QuantumApplicationLab/hhl-prototype?branch=main)
+
+
+# HHL Quantum Linear Solver
+The `hhl-prototype` allows to use the HHL algorithm to solve linear system of euqations. 
+Original implementation was implemented by [@anedumla](https://github.com/anedumla) : https://github.com/anedumla/quantum_linear_solvers
 
 ## Installation
 ```python
@@ -9,5 +18,26 @@ cd hhl_prototype
 pip install -e .
 ```
 
+## Use
+
+```python
+import numpy as np
+from hhl_prototype import HHL
+from qiskit.primitives import Estimator, Sampler
+
+# create the matrix
+A = np.random.rand(4,4)
+A = A+A.T
+
+# create the right hand side 
+b = np.random.rand(4)
+
+# create the solver and solve
+hhl = HHL(estimator=Estimator(), sampler=Sampler())
+hhl.solve(A, b)
+```
+
+
+
 ## Documentation
 Tutorial: https://qiskit.org/textbook/ch-applications/hhl_tutorial.html

docs/hhl.py

@@ -1,10 +1,10 @@
 import numpy as np
 from hhl_prototype import HHL
 
-A = np.random.rand(4,4)
-A = A+A.T
+A = np.random.rand(4, 4)
+A = A + A.T
 
 b = np.random.rand(4)
 
 hhl = HHL()
-hhl.solve(A, b)
+hhl.solve(A, b)

hhl_prototype/__init__.py

@@ -71,4 +71,3 @@
     MatrixFunctional
 
 """
-

hhl_prototype/observables/absolute_average.py

@@ -16,7 +16,8 @@
 import numpy as np
 
 from qiskit import QuantumCircuit
-#from qiskit.opflow import I, Z, TensoredOp
+
+# from qiskit.opflow import I, Z, TensoredOp
 from qiskit.quantum_info import SparsePauliOp as TensoredOp
 
 from qiskit.quantum_info import Statevector

hhl_prototype/observables/linear_system_observable.py

@@ -17,7 +17,8 @@
 import numpy as np
 
 from qiskit import QuantumCircuit
-#from qiskit.opflow import TensoredOp
+
+# from qiskit.opflow import TensoredOp
 from qiskit.quantum_info import SparsePauliOp as TensoredOp
 
 

hhl_prototype/observables/matrix_functional.py

@@ -18,7 +18,8 @@
 
 from qiskit import QuantumCircuit
 from qiskit.quantum_info import Statevector
-#from qiskit.opflow import I, Z, TensoredOp
+
+# from qiskit.opflow import I, Z, TensoredOp
 from qiskit.quantum_info import SparsePauliOp as TensoredOp
 
 from .linear_system_observable import LinearSystemObservable

hhl_prototype/solver/hhl.py

@@ -72,11 +72,11 @@ def __init__(
         estimator: BaseEstimator,
         sampler: Optional[Union[BaseSampler, None]] = None,
         epsilon: float = 1e-2,
-        ) -> None:
+    ) -> None:
         r"""
         Args:
             estimator: Estimator used for running circuit. Default is BaseEstimator
-            sampler: Sampler used for running circuit, overrides estimator. Default is None. 
+            sampler: Sampler used for running circuit, overrides estimator. Default is None.
             epsilon: Error tolerance of the approximation to the solution, i.e. if :math:`x` is the
                 exact solution and :math:`\tilde{x}` the one calculated by the algorithm, then
                 :math:`||x - \tilde{x}|| \le epsilon`.
@@ -152,7 +152,7 @@ def construct_circuit(
                 vector = np.array(vector)
             nb = int(np.log2(len(vector)))
             vector_circuit = QuantumCircuit(nb)
-            #prepare the vector b in the first quantum register
+            # prepare the vector b in the first quantum register
 
             isometry = Isometry(vector / np.linalg.norm(vector), 0, 0)
             vector_circuit.append(isometry, list(range(nb)))

test/test_hhl.py

@@ -24,10 +24,9 @@ def create_random_matrix(size: int) -> sparray:
 @pytest.mark.parametrize("b", [np.random.rand(size)])
 @pytest.mark.parametrize("estimator", [Estimator()])
 @pytest.mark.parametrize("sampler", [Sampler()])
-
 def test_hhl_solve_default(A, b, estimator, sampler):
     """Test the hhl solver."""
-    hhl = HHL(estimator, sampler = sampler)
-    results = hhl.solve(A,b).solution
+    hhl = HHL(estimator, sampler=sampler)
+    results = hhl.solve(A, b).solution
     if np.linalg.norm(A.dot(results) - b) > 0.1:
         pytest.skip("HHL solution innacurate")

tox.ini

@@ -0,0 +1,35 @@
+[tox]
+minversion = 2.1
+envlist = py38, py39, py310, lint, coverage
+# CI: skip-next-line
+skip_missing_interpreters = true
+
+[testenv]
+# CI: skip-next-line
+usedevelop = true
+install_command = pip install -U {opts} {packages}
+setenv =
+  VIRTUAL_ENV={envdir}
+  LANGUAGE=en_US
+  LC_ALL=en_US.utf-8
+extras = dev
+commands =
+  pip check
+  python -m pytest -v --doctest-modules
+  treon docs --threads 2
+
+[testenv:black]
+envdir = .tox/lint
+skip_install = true
+commands = black .
+
+[testenv:coverage]
+basepython = python3
+setenv =
+  {[testenv]setenv}
+commands =
+  coverage3 run --source hhl_prototype --parallel-mode -m pytest --doctest-modules
+  coverage3 combine
+  coverage3 report
+
+