%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 * Tue May 30 2023 Python_Bot - 0.3.4-1 - Package Spec generated