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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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.4-1
- Package Spec generated
|
