automatic import of python-qiskit-finance

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+/qiskit-finance-0.3.4.tar.gz
diff --git a/python-qiskit-finance.spec b/python-qiskit-finance.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-qiskit-finance
+Version:	0.3.4
+Release:	1
+Summary:	Qiskit Finance: A library of quantum computing finance experiments
+License:	Apache-2.0
+URL:		https://github.com/Qiskit/qiskit-finance
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/74/79/2884c8415315099e254f0d83d558af862724a4ab5cb2d72a7979030943c2/qiskit-finance-0.3.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-qiskit-terra
+Requires:	python3-qiskit-optimization
+Requires:	python3-scipy
+Requires:	python3-numpy
+Requires:	python3-psutil
+Requires:	python3-fastdtw
+Requires:	python3-setuptools
+Requires:	python3-pandas
+Requires:	python3-nasdaq-data-link
+Requires:	python3-yfinance
+Requires:	python3-certifi
+Requires:	python3-urllib3
+
+%description
+### Creating Your First Finance Programming Experiment in Qiskit
+Now that Qiskit Finance is installed, it's time to begin working with the finance module.
+Let's try an experiment using Amplitude Estimation algorithm to
+evaluate a fixed income asset with uncertain interest rates.
+```python
+import numpy as np
+from qiskit import BasicAer
+from qiskit.algorithms import AmplitudeEstimation
+from qiskit_finance.circuit.library import NormalDistribution
+from qiskit_finance.applications import FixedIncomePricing
+# Create a suitable multivariate distribution
+num_qubits = [2, 2]
+bounds = [(0, 0.12), (0, 0.24)]
+mvnd = NormalDistribution(
+    num_qubits, mu=[0.12, 0.24], sigma=0.01 * np.eye(2), bounds=bounds
+)
+# Create fixed income component
+fixed_income = FixedIncomePricing(
+    num_qubits,
+    np.eye(2),
+    np.zeros(2),
+    cash_flow=[1.0, 2.0],
+    rescaling_factor=0.125,
+    bounds=bounds,
+    uncertainty_model=mvnd,
+)
+# the FixedIncomeExpectedValue provides us with the necessary rescalings
+# create the A operator for amplitude estimation
+problem = fixed_income.to_estimation_problem()
+# Set number of evaluation qubits (samples)
+num_eval_qubits = 5
+# Construct and run amplitude estimation
+q_i = BasicAer.get_backend("statevector_simulator")
+algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits, quantum_instance=q_i)
+result = algo.estimate(problem)
+print(f"Estimated value:\t{fixed_income.interpret(result):.4f}")
+print(f"Probability:    \t{result.max_probability:.4f}")
+```
+When running the above the estimated value result should be 2.46 and probability 0.8487.
+### Further examples
+Learning path notebooks may be found in the
+[finance tutorials](https://qiskit.org/documentation/finance/tutorials/index.html) section
+
+%package -n python3-qiskit-finance
+Summary:	Qiskit Finance: A library of quantum computing finance experiments
+Provides:	python-qiskit-finance
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-qiskit-finance
+### Creating Your First Finance Programming Experiment in Qiskit
+Now that Qiskit Finance is installed, it's time to begin working with the finance module.
+Let's try an experiment using Amplitude Estimation algorithm to
+evaluate a fixed income asset with uncertain interest rates.
+```python
+import numpy as np
+from qiskit import BasicAer
+from qiskit.algorithms import AmplitudeEstimation
+from qiskit_finance.circuit.library import NormalDistribution
+from qiskit_finance.applications import FixedIncomePricing
+# Create a suitable multivariate distribution
+num_qubits = [2, 2]
+bounds = [(0, 0.12), (0, 0.24)]
+mvnd = NormalDistribution(
+    num_qubits, mu=[0.12, 0.24], sigma=0.01 * np.eye(2), bounds=bounds
+)
+# Create fixed income component
+fixed_income = FixedIncomePricing(
+    num_qubits,
+    np.eye(2),
+    np.zeros(2),
+    cash_flow=[1.0, 2.0],
+    rescaling_factor=0.125,
+    bounds=bounds,
+    uncertainty_model=mvnd,
+)
+# the FixedIncomeExpectedValue provides us with the necessary rescalings
+# create the A operator for amplitude estimation
+problem = fixed_income.to_estimation_problem()
+# Set number of evaluation qubits (samples)
+num_eval_qubits = 5
+# Construct and run amplitude estimation
+q_i = BasicAer.get_backend("statevector_simulator")
+algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits, quantum_instance=q_i)
+result = algo.estimate(problem)
+print(f"Estimated value:\t{fixed_income.interpret(result):.4f}")
+print(f"Probability:    \t{result.max_probability:.4f}")
+```
+When running the above the estimated value result should be 2.46 and probability 0.8487.
+### Further examples
+Learning path notebooks may be found in the
+[finance tutorials](https://qiskit.org/documentation/finance/tutorials/index.html) section
+
+%package help
+Summary:	Development documents and examples for qiskit-finance
+Provides:	python3-qiskit-finance-doc
+%description help
+### Creating Your First Finance Programming Experiment in Qiskit
+Now that Qiskit Finance is installed, it's time to begin working with the finance module.
+Let's try an experiment using Amplitude Estimation algorithm to
+evaluate a fixed income asset with uncertain interest rates.
+```python
+import numpy as np
+from qiskit import BasicAer
+from qiskit.algorithms import AmplitudeEstimation
+from qiskit_finance.circuit.library import NormalDistribution
+from qiskit_finance.applications import FixedIncomePricing
+# Create a suitable multivariate distribution
+num_qubits = [2, 2]
+bounds = [(0, 0.12), (0, 0.24)]
+mvnd = NormalDistribution(
+    num_qubits, mu=[0.12, 0.24], sigma=0.01 * np.eye(2), bounds=bounds
+)
+# Create fixed income component
+fixed_income = FixedIncomePricing(
+    num_qubits,
+    np.eye(2),
+    np.zeros(2),
+    cash_flow=[1.0, 2.0],
+    rescaling_factor=0.125,
+    bounds=bounds,
+    uncertainty_model=mvnd,
+)
+# the FixedIncomeExpectedValue provides us with the necessary rescalings
+# create the A operator for amplitude estimation
+problem = fixed_income.to_estimation_problem()
+# Set number of evaluation qubits (samples)
+num_eval_qubits = 5
+# Construct and run amplitude estimation
+q_i = BasicAer.get_backend("statevector_simulator")
+algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits, quantum_instance=q_i)
+result = algo.estimate(problem)
+print(f"Estimated value:\t{fixed_income.interpret(result):.4f}")
+print(f"Probability:    \t{result.max_probability:.4f}")
+```
+When running the above the estimated value result should be 2.46 and probability 0.8487.
+### Further examples
+Learning path notebooks may be found in the
+[finance tutorials](https://qiskit.org/documentation/finance/tutorials/index.html) section
+
+%prep
+%autosetup -n qiskit-finance-0.3.4
+
+%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-finance -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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+++ b/sources
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+d3e24980dbc9f3958a026fe17e17726a  qiskit-finance-0.3.4.tar.gz