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@@ -0,0 +1 @@ +/mthree-2.5.1.tar.gz diff --git a/python-mthree.spec b/python-mthree.spec new file mode 100644 index 0000000..f872a61 --- /dev/null +++ b/python-mthree.spec @@ -0,0 +1,455 @@ +%global _empty_manifest_terminate_build 0 +Name: python-mthree +Version: 2.5.1 +Release: 1 +Summary: M3: Matrix-free measurement mitigation +License: Apache 2.0 +URL: https://pypi.org/project/mthree/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/eb/a8/c63caea37476790a552698b867bb151734f9139406ef2dfb4ec2324e7aaf/mthree-2.5.1.tar.gz + + +%description +# mthree + +[](https://opensource.org/licenses/Apache-2.0) +[](https://badge.fury.io/py/mthree) +[](https://pypi.org/project/mthree/) + + +Matrix-free Measurement Mitigation (M3). + +M3 is a measurement mitigation technique that solves for corrected measurement probabilities using a dimensionality reduction step followed by either direct LU factorization or a preconditioned iterative method that nominally converges in O(1) steps, and can be computed in parallel. For example, M3 can compute corrections on 42 qubit GHZ problems in under two seconds on a quad-core machine (depending on the number of unique bitstrings in the output). + +## Documentation + +[Online Documentation @ Qiskit.org](https://qiskit.org/ecosystem/mthree/) + +## Installation + +You can `pip` install M3 in serial mode using PyPi via: + +```bash +pip install mthree +``` + +Alternatively, one can install from source: + +```bash +python setup.py install +``` + +To enable openmp one must have an openmp 3.0+ enabled compiler and install with: + +```bash +python setup.py install --with-openmp +``` + +Optionally one can also set `-march=native` using: + +```bash +python setup.py install --with-native +``` + +The `openmp` and `native` flags can be used simultaneously using a comma. + +### OpenMP on OSX + +On OSX one must install LLVM using homebrew (You cannot use GCC): + +```bash +brew install llvm +``` + +after which the following (or the like) must be executed in the terminal: + +```bash +export PATH="/usr/local/opt/llvm/bin:$PATH" +``` + +and + +```bash +export LDFLAGS="-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib" +export CPPFLAGS="-I/usr/local/opt/llvm/include" +``` + +Then installation with openmp can be accomplished using: + +```bash +CC=clang CXX=clang python setup.py install --with-openmp +``` + +## Usage + +### Basic usage + +M3 is simple to use: + +```python +import mthree +# Specify a mitigator object targeting a given backend +mit = mthree.M3Mitigation(backend) + +# Compute the 1Q calibration matrices for the given qubits and given number of shots +# By default it is over all backend qubits at 10000 shots. +mit.cals_from_system(qubits, shots) + +# Apply mitigation to a given dict of raw counts over the specified qubits +m3_quasi = mit.apply_correction(raw_counts, qubits) +``` + +Note that here `qubits` is a list of which qubits are measured to yield the bits in the output. +For example the list `[4,3,1,2,0]` indicates that a measurement on physical qubit 4 was written to +classical bit zero in the output bit-strings, physical qubit 3 maps to classical bit 1, etc. +The fact that the zeroth bit is right-most in the bitstring is handled internally. + +### Error bounds + +It is possible to compute error bounds in a similarly efficient manner. This is not done +by default, but rather turned on using: + +```python +m3_quasi = mit.apply_correction(raw_counts, qubits, return_mitigation_overhead=True) +``` + +Then the distribution itself can be called to return things like the expectation value and the +standard deviation: + +```python +expval, stddev = quasi.expval_and_stddev() +``` + +### Closest probability distribution + +The results of M3 mitigation are quasi-probabilities that nominally contain small negative values. +This is suitable for use in computing corrected expectation values or sampling problems +where one is interested in the highest probability bit-string. However, if one needs +a true probability distribution then it is possible to convert from quasi-probabilites to +the closest true probability distribution in L2-norm using: + +```python +closest_probs = m3_quasi.nearest_probability_distribution() +``` + +## License + +[](https://opensource.org/licenses/Apache-2.0) + + + + +%package -n python3-mthree +Summary: M3: Matrix-free measurement mitigation +Provides: python-mthree +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-mthree +# mthree + +[](https://opensource.org/licenses/Apache-2.0) +[](https://badge.fury.io/py/mthree) +[](https://pypi.org/project/mthree/) + + +Matrix-free Measurement Mitigation (M3). + +M3 is a measurement mitigation technique that solves for corrected measurement probabilities using a dimensionality reduction step followed by either direct LU factorization or a preconditioned iterative method that nominally converges in O(1) steps, and can be computed in parallel. For example, M3 can compute corrections on 42 qubit GHZ problems in under two seconds on a quad-core machine (depending on the number of unique bitstrings in the output). + +## Documentation + +[Online Documentation @ Qiskit.org](https://qiskit.org/ecosystem/mthree/) + +## Installation + +You can `pip` install M3 in serial mode using PyPi via: + +```bash +pip install mthree +``` + +Alternatively, one can install from source: + +```bash +python setup.py install +``` + +To enable openmp one must have an openmp 3.0+ enabled compiler and install with: + +```bash +python setup.py install --with-openmp +``` + +Optionally one can also set `-march=native` using: + +```bash +python setup.py install --with-native +``` + +The `openmp` and `native` flags can be used simultaneously using a comma. + +### OpenMP on OSX + +On OSX one must install LLVM using homebrew (You cannot use GCC): + +```bash +brew install llvm +``` + +after which the following (or the like) must be executed in the terminal: + +```bash +export PATH="/usr/local/opt/llvm/bin:$PATH" +``` + +and + +```bash +export LDFLAGS="-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib" +export CPPFLAGS="-I/usr/local/opt/llvm/include" +``` + +Then installation with openmp can be accomplished using: + +```bash +CC=clang CXX=clang python setup.py install --with-openmp +``` + +## Usage + +### Basic usage + +M3 is simple to use: + +```python +import mthree +# Specify a mitigator object targeting a given backend +mit = mthree.M3Mitigation(backend) + +# Compute the 1Q calibration matrices for the given qubits and given number of shots +# By default it is over all backend qubits at 10000 shots. +mit.cals_from_system(qubits, shots) + +# Apply mitigation to a given dict of raw counts over the specified qubits +m3_quasi = mit.apply_correction(raw_counts, qubits) +``` + +Note that here `qubits` is a list of which qubits are measured to yield the bits in the output. +For example the list `[4,3,1,2,0]` indicates that a measurement on physical qubit 4 was written to +classical bit zero in the output bit-strings, physical qubit 3 maps to classical bit 1, etc. +The fact that the zeroth bit is right-most in the bitstring is handled internally. + +### Error bounds + +It is possible to compute error bounds in a similarly efficient manner. This is not done +by default, but rather turned on using: + +```python +m3_quasi = mit.apply_correction(raw_counts, qubits, return_mitigation_overhead=True) +``` + +Then the distribution itself can be called to return things like the expectation value and the +standard deviation: + +```python +expval, stddev = quasi.expval_and_stddev() +``` + +### Closest probability distribution + +The results of M3 mitigation are quasi-probabilities that nominally contain small negative values. +This is suitable for use in computing corrected expectation values or sampling problems +where one is interested in the highest probability bit-string. However, if one needs +a true probability distribution then it is possible to convert from quasi-probabilites to +the closest true probability distribution in L2-norm using: + +```python +closest_probs = m3_quasi.nearest_probability_distribution() +``` + +## License + +[](https://opensource.org/licenses/Apache-2.0) + + + + +%package help +Summary: Development documents and examples for mthree +Provides: python3-mthree-doc +%description help +# mthree + +[](https://opensource.org/licenses/Apache-2.0) +[](https://badge.fury.io/py/mthree) +[](https://pypi.org/project/mthree/) + + +Matrix-free Measurement Mitigation (M3). + +M3 is a measurement mitigation technique that solves for corrected measurement probabilities using a dimensionality reduction step followed by either direct LU factorization or a preconditioned iterative method that nominally converges in O(1) steps, and can be computed in parallel. For example, M3 can compute corrections on 42 qubit GHZ problems in under two seconds on a quad-core machine (depending on the number of unique bitstrings in the output). + +## Documentation + +[Online Documentation @ Qiskit.org](https://qiskit.org/ecosystem/mthree/) + +## Installation + +You can `pip` install M3 in serial mode using PyPi via: + +```bash +pip install mthree +``` + +Alternatively, one can install from source: + +```bash +python setup.py install +``` + +To enable openmp one must have an openmp 3.0+ enabled compiler and install with: + +```bash +python setup.py install --with-openmp +``` + +Optionally one can also set `-march=native` using: + +```bash +python setup.py install --with-native +``` + +The `openmp` and `native` flags can be used simultaneously using a comma. + +### OpenMP on OSX + +On OSX one must install LLVM using homebrew (You cannot use GCC): + +```bash +brew install llvm +``` + +after which the following (or the like) must be executed in the terminal: + +```bash +export PATH="/usr/local/opt/llvm/bin:$PATH" +``` + +and + +```bash +export LDFLAGS="-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib" +export CPPFLAGS="-I/usr/local/opt/llvm/include" +``` + +Then installation with openmp can be accomplished using: + +```bash +CC=clang CXX=clang python setup.py install --with-openmp +``` + +## Usage + +### Basic usage + +M3 is simple to use: + +```python +import mthree +# Specify a mitigator object targeting a given backend +mit = mthree.M3Mitigation(backend) + +# Compute the 1Q calibration matrices for the given qubits and given number of shots +# By default it is over all backend qubits at 10000 shots. +mit.cals_from_system(qubits, shots) + +# Apply mitigation to a given dict of raw counts over the specified qubits +m3_quasi = mit.apply_correction(raw_counts, qubits) +``` + +Note that here `qubits` is a list of which qubits are measured to yield the bits in the output. +For example the list `[4,3,1,2,0]` indicates that a measurement on physical qubit 4 was written to +classical bit zero in the output bit-strings, physical qubit 3 maps to classical bit 1, etc. +The fact that the zeroth bit is right-most in the bitstring is handled internally. + +### Error bounds + +It is possible to compute error bounds in a similarly efficient manner. This is not done +by default, but rather turned on using: + +```python +m3_quasi = mit.apply_correction(raw_counts, qubits, return_mitigation_overhead=True) +``` + +Then the distribution itself can be called to return things like the expectation value and the +standard deviation: + +```python +expval, stddev = quasi.expval_and_stddev() +``` + +### Closest probability distribution + +The results of M3 mitigation are quasi-probabilities that nominally contain small negative values. +This is suitable for use in computing corrected expectation values or sampling problems +where one is interested in the highest probability bit-string. However, if one needs +a true probability distribution then it is possible to convert from quasi-probabilites to +the closest true probability distribution in L2-norm using: + +```python +closest_probs = m3_quasi.nearest_probability_distribution() +``` + +## License + +[](https://opensource.org/licenses/Apache-2.0) + + + + +%prep +%autosetup -n mthree-2.5.1 + +%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-mthree -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.1-1 +- Package Spec generated @@ -0,0 +1 @@ +5e9a98603cad56cbe8215f4e3e0aab5e mthree-2.5.1.tar.gz |