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@@ -0,0 +1 @@ +/qiskit-machine-learning-0.6.1.tar.gz diff --git a/python-qiskit-machine-learning.spec b/python-qiskit-machine-learning.spec new file mode 100644 index 0000000..f46608f --- /dev/null +++ b/python-qiskit-machine-learning.spec @@ -0,0 +1,235 @@ +%global _empty_manifest_terminate_build 0 +Name: python-qiskit-machine-learning +Version: 0.6.1 +Release: 1 +Summary: Qiskit Machine Learning: A library of quantum computing machine learning experiments +License: Apache-2.0 +URL: https://github.com/Qiskit/qiskit-machine-learning +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/09/c6/502e29169953df4a7c3f8539858d3546877198bf7cafa6318361d5d381c4/qiskit-machine-learning-0.6.1.tar.gz +BuildArch: noarch + +Requires: python3-qiskit-terra +Requires: python3-scipy +Requires: python3-numpy +Requires: python3-psutil +Requires: python3-scikit-learn +Requires: python3-fastdtw +Requires: python3-setuptools +Requires: python3-dill +Requires: python3-sparse +Requires: python3-torch + +%description +### Optional Installs +* **PyTorch**, may be installed either using command `pip install 'qiskit-machine-learning[torch]'` to install the + package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/). When PyTorch + is installed, the `TorchConnector` facilitates its use of quantum computed networks. +* **Sparse**, may be installed using command `pip install 'qiskit-machine-learning[sparse]'` to install the + package. Sparse being installed will enable the usage of sparse arrays/tensors. +### Creating Your First Machine Learning Programming Experiment in Qiskit +Now that Qiskit Machine Learning is installed, it's time to begin working with the Machine Learning module. +Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to +train and test samples from a data set to see how accurately the test set can +be classified. +```python +from qiskit.algorithms.optimizers import COBYLA +from qiskit.circuit.library import TwoLocal, ZZFeatureMap +from qiskit.utils import algorithm_globals +from qiskit_machine_learning.algorithms import VQC +from qiskit_machine_learning.datasets import ad_hoc_data +seed = 1376 +algorithm_globals.random_seed = seed +# Use ad hoc data set for training and test data +feature_dim = 2 # dimension of each data point +training_size = 20 +test_size = 10 +# training features, training labels, test features, test labels as np.ndarray, +# one hot encoding for labels +training_features, training_labels, test_features, test_labels = ad_hoc_data( + training_size=training_size, test_size=test_size, n=feature_dim, gap=0.3 +) +feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement="linear") +ansatz = TwoLocal(feature_map.num_qubits, ["ry", "rz"], "cz", reps=3) +vqc = VQC( + feature_map=feature_map, + ansatz=ansatz, + optimizer=COBYLA(maxiter=100), +) +vqc.fit(training_features, training_labels) +score = vqc.score(test_features, test_labels) +print(f"Testing accuracy: {score:0.2f}") +``` +### Further examples +Learning path notebooks may be found in the +[Machine Learning tutorials](https://qiskit.org/documentation/machine-learning/tutorials/index.html) section +of the documentation and are a great place to start. +Another good place to learn the fundamentals of quantum machine learning is the +[Quantum Machine Learning](https://learn.qiskit.org/course/machine-learning/introduction) course +on the Qiskit Textbook's website. The course is very convenient for beginners who are eager to learn +quantum machine learning from scratch, as well as understand the background and theory behind algorithms in +Qiskit Machine Learning. The course covers a variety of topics to build understanding of parameterized +circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine +learning - how to build and train quantum ML models for supervised and unsupervised learning. +The textbook course is complementary to the tutorials of this module, where the tutorials focus +on actual Qiskit Machine Learning algorithms, the course more explains and details underlying fundamentals + +%package -n python3-qiskit-machine-learning +Summary: Qiskit Machine Learning: A library of quantum computing machine learning experiments +Provides: python-qiskit-machine-learning +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-qiskit-machine-learning +### Optional Installs +* **PyTorch**, may be installed either using command `pip install 'qiskit-machine-learning[torch]'` to install the + package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/). When PyTorch + is installed, the `TorchConnector` facilitates its use of quantum computed networks. +* **Sparse**, may be installed using command `pip install 'qiskit-machine-learning[sparse]'` to install the + package. Sparse being installed will enable the usage of sparse arrays/tensors. +### Creating Your First Machine Learning Programming Experiment in Qiskit +Now that Qiskit Machine Learning is installed, it's time to begin working with the Machine Learning module. +Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to +train and test samples from a data set to see how accurately the test set can +be classified. +```python +from qiskit.algorithms.optimizers import COBYLA +from qiskit.circuit.library import TwoLocal, ZZFeatureMap +from qiskit.utils import algorithm_globals +from qiskit_machine_learning.algorithms import VQC +from qiskit_machine_learning.datasets import ad_hoc_data +seed = 1376 +algorithm_globals.random_seed = seed +# Use ad hoc data set for training and test data +feature_dim = 2 # dimension of each data point +training_size = 20 +test_size = 10 +# training features, training labels, test features, test labels as np.ndarray, +# one hot encoding for labels +training_features, training_labels, test_features, test_labels = ad_hoc_data( + training_size=training_size, test_size=test_size, n=feature_dim, gap=0.3 +) +feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement="linear") +ansatz = TwoLocal(feature_map.num_qubits, ["ry", "rz"], "cz", reps=3) +vqc = VQC( + feature_map=feature_map, + ansatz=ansatz, + optimizer=COBYLA(maxiter=100), +) +vqc.fit(training_features, training_labels) +score = vqc.score(test_features, test_labels) +print(f"Testing accuracy: {score:0.2f}") +``` +### Further examples +Learning path notebooks may be found in the +[Machine Learning tutorials](https://qiskit.org/documentation/machine-learning/tutorials/index.html) section +of the documentation and are a great place to start. +Another good place to learn the fundamentals of quantum machine learning is the +[Quantum Machine Learning](https://learn.qiskit.org/course/machine-learning/introduction) course +on the Qiskit Textbook's website. The course is very convenient for beginners who are eager to learn +quantum machine learning from scratch, as well as understand the background and theory behind algorithms in +Qiskit Machine Learning. The course covers a variety of topics to build understanding of parameterized +circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine +learning - how to build and train quantum ML models for supervised and unsupervised learning. +The textbook course is complementary to the tutorials of this module, where the tutorials focus +on actual Qiskit Machine Learning algorithms, the course more explains and details underlying fundamentals + +%package help +Summary: Development documents and examples for qiskit-machine-learning +Provides: python3-qiskit-machine-learning-doc +%description help +### Optional Installs +* **PyTorch**, may be installed either using command `pip install 'qiskit-machine-learning[torch]'` to install the + package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/). When PyTorch + is installed, the `TorchConnector` facilitates its use of quantum computed networks. +* **Sparse**, may be installed using command `pip install 'qiskit-machine-learning[sparse]'` to install the + package. Sparse being installed will enable the usage of sparse arrays/tensors. +### Creating Your First Machine Learning Programming Experiment in Qiskit +Now that Qiskit Machine Learning is installed, it's time to begin working with the Machine Learning module. +Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to +train and test samples from a data set to see how accurately the test set can +be classified. +```python +from qiskit.algorithms.optimizers import COBYLA +from qiskit.circuit.library import TwoLocal, ZZFeatureMap +from qiskit.utils import algorithm_globals +from qiskit_machine_learning.algorithms import VQC +from qiskit_machine_learning.datasets import ad_hoc_data +seed = 1376 +algorithm_globals.random_seed = seed +# Use ad hoc data set for training and test data +feature_dim = 2 # dimension of each data point +training_size = 20 +test_size = 10 +# training features, training labels, test features, test labels as np.ndarray, +# one hot encoding for labels +training_features, training_labels, test_features, test_labels = ad_hoc_data( + training_size=training_size, test_size=test_size, n=feature_dim, gap=0.3 +) +feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement="linear") +ansatz = TwoLocal(feature_map.num_qubits, ["ry", "rz"], "cz", reps=3) +vqc = VQC( + feature_map=feature_map, + ansatz=ansatz, + optimizer=COBYLA(maxiter=100), +) +vqc.fit(training_features, training_labels) +score = vqc.score(test_features, test_labels) +print(f"Testing accuracy: {score:0.2f}") +``` +### Further examples +Learning path notebooks may be found in the +[Machine Learning tutorials](https://qiskit.org/documentation/machine-learning/tutorials/index.html) section +of the documentation and are a great place to start. +Another good place to learn the fundamentals of quantum machine learning is the +[Quantum Machine Learning](https://learn.qiskit.org/course/machine-learning/introduction) course +on the Qiskit Textbook's website. The course is very convenient for beginners who are eager to learn +quantum machine learning from scratch, as well as understand the background and theory behind algorithms in +Qiskit Machine Learning. The course covers a variety of topics to build understanding of parameterized +circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine +learning - how to build and train quantum ML models for supervised and unsupervised learning. +The textbook course is complementary to the tutorials of this module, where the tutorials focus +on actual Qiskit Machine Learning algorithms, the course more explains and details underlying fundamentals + +%prep +%autosetup -n qiskit-machine-learning-0.6.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-qiskit-machine-learning -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.1-1 +- Package Spec generated @@ -0,0 +1 @@ +9d0e8d42754d9fa9c61ff47557dd043f qiskit-machine-learning-0.6.1.tar.gz |