Fix issues with CVXPY default solvers (qiskit-community#422)

- Fixes deprecated multiplication operators
- Adds a check to set default SDP solver based on installed solvers in cvxpy, preferring 'CVXOP' if installed, then 'SCS'.
 - Changes auto method of tomography fitter to only use 'cvx' when a default solver is sound.

When checking if 'SCS' is installed, this runs a short optimization problem to see if SCS is built with BLAS support, since it cannot solve problems bigger than 2x2 matrices without it.


* Change tomography solver to check for installed CVX solvers

* Fix cvxpy deprecation warnings

* Fix check for SDP solver

* Add cvxopt solver to test envs

* Fix typo

* and another

* Install cvxopt from conda-forge

.travis.yml

@@ -114,7 +114,7 @@ language: python
 install: pip install -U tox pip virtualenv setuptools six
 script:
   - tox --notest
-  - .tox/$TOXENV/bin/pip install --no-cache-dir --ignore-installed 'scs<2.1.2'
+  - .tox/$TOXENV/bin/pip install --no-cache-dir cvxopt
   - tox
 
 notifications:

azure-pipelines.yml

@@ -48,7 +48,6 @@ jobs:
           Python35:
             python.version: '3.5'
             TOXENV: py35
-            OPENBLAS: https://3f23b170c54c2533c070-1c8a9b3114517dc5fe17b7c3f8c63a43.ssl.cf2.rackcdn.com/openblas-5f998ef_gcc7_1_0_win64.zip
           Python36:
             python.version: '3.6'
             TOXENV: py36
@@ -66,44 +65,8 @@ jobs:
           displayName: Create Anaconda environment
         - script: |
             call activate qiskit-ignis
-            conda install --yes --quiet --name qiskit-ignis --channel conda-forge python=%PYTHON_VERSION% scs
-          displayName: Install Anaconda packages
-          condition: ne(variables['python.version'], '3.5')
-        - script: |
-            call activate qiskit-ignis
-            conda install --yes --quiet --name qiskit-ignis --channel conda-forge python=%PYTHON_VERSION% osqp
-          displayName: Install Anaconda packages
-          condition: eq(variables['python.version'], '3.8')
-        - powershell: (new-object System.Net.WebClient).DownloadFile($env:OPENBLAS, 'c:\openblas.zip')
-          condition: eq(variables['python.version'], '3.5')
-          displayName: 'Download openblas'
-        - powershell: Expand-Archive c:\openblas.zip c:\openblas
-          condition: eq(variables['python.version'], '3.5')
-          displayName: 'Unzip openblas'
-        - script: |
-            call activate qiskit-ignis
-            conda install --yes --quiet --update-all --name qiskit-ignis python=%PYTHON_VERSION% numpy scipy
-          displayName: Install Anaconda packages
-          condition: eq(variables['python.version'], '3.5')
-        - script: |
-            call activate qiskit-ignis
-            set PATH=%PATH%;%BINPATH%;C:\openblas\%64%\bin;
-            echo %PATH%
-            dir C:\openblas\%ARCH%\bin
-            make.exe CC="gcc.exe -m64" USE_LAPACK=1 BLASLDFLAGS="-LC:\openblas\64\bin -lopenblas_5f998ef_gcc7_1_0"
-            make.exe install
-            git clone --recursive https://github.com/bodono/scs-python.git
-            cd scs-python
-            python setup.py install --scs 'CC="gcc.exe -m64" USE_LAPACK=1 BLASLDFLAGS="-LC:\openblas\64\bin -lopenblas_5f998ef_gcc7_1_0"'
-            cd ..
-          condition: eq(variables['python.version'], '3.5')
-          displayName: 'Install scs from source for python 3.5'
-        - script: |
-            call activate qiskit-ignis
-            conda install --yes --quiet --name qiskit-ignis python=%PYTHON_VERSION% mkl
-          displayName: 'Install MKL'
-        - script: |
-            call activate qiskit-ignis
+            conda config --add channels conda-forge
+            conda install cvxopt
             python -m pip install -c constraints.txt --upgrade pip virtualenv setuptools
             pip install -c constraints.txt -U tox
             tox --sitepackages -e%TOXENV%

qiskit/ignis/verification/tomography/fitters/base_fitter.py

@@ -29,8 +29,8 @@
 from qiskit.result import Result
 from ..basis import TomographyBasis, default_basis
 from ..data import marginal_counts, combine_counts, count_keys
-from .cvx_fit import cvxpy, cvx_fit
 from .lstsq_fit import lstsq_fit
+from .cvx_fit import cvx_fit, _HAS_CVX
 
 # Create logger
 logger = logging.getLogger(__name__)
@@ -39,6 +39,8 @@
 class TomographyFitter:
     """Base maximum-likelihood estimate tomography fitter class"""
 
+    _HAS_SDP_SOLVER = None
+
     def __init__(self,
                  result: Result,
                  circuits: Union[List[QuantumCircuit], List[str]],
@@ -195,10 +197,11 @@ def fit(self,
                                                         beta)
         # Choose automatic method
         if method == 'auto':
-            if cvxpy is None:
-                method = 'lstsq'
-            else:
+            self._check_for_sdp_solver()
+            if self._HAS_SDP_SOLVER:
                 method = 'cvx'
+            else:
+                method = 'lstsq'
         if method == 'lstsq':
             return lstsq_fit(data, basis_matrix,
                              weights=weights,
@@ -486,3 +489,28 @@ def _measurement_ops(self,
                 op = np.kron(op, meas_matrix_fn(m, outcome))
             meas_ops.append(op)
         return meas_ops
+
+    @classmethod
+    def _check_for_sdp_solver(cls):
+        """Check if CVXPY solver is available"""
+        if cls._HAS_SDP_SOLVER is None:
+            if _HAS_CVX:
+                # pylint:disable=import-error
+                import cvxpy
+                solvers = cvxpy.installed_solvers()
+                if 'CVXOPT' in solvers:
+                    cls._HAS_SDP_SOLVER = True
+                    return
+                if 'SCS' in solvers:
+                    # Try example problem to see if built with BLAS
+                    # SCS solver cannot solver larger than 2x2 matrix
+                    # problems without BLAS
+                    try:
+                        var = cvxpy.Variable((4, 4), PSD=True)
+                        obj = cvxpy.Minimize(cvxpy.norm(var))
+                        cvxpy.Problem(obj).solve(solver='SCS')
+                        cls._HAS_SDP_SOLVER = True
+                        return
+                    except cvxpy.error.SolverError:
+                        pass
+            cls._HAS_SDP_SOLVER = False

qiskit/ignis/verification/tomography/fitters/cvx_fit.py

@@ -24,8 +24,9 @@
 # Check if CVXPY package is installed
 try:
     import cvxpy
+    _HAS_CVX = True
 except ImportError:
-    cvxpy = None
+    _HAS_CVX = False
 
 
 def cvx_fit(data: np.array,
@@ -108,7 +109,7 @@ def cvx_fit(data: np.array,
     """
 
     # Check if CVXPY package is installed
-    if cvxpy is None:
+    if not _HAS_CVX:
         raise ImportError("The CVXPY package is required to use the cvx_fit() "
                           "function. You can install it with 'pip install "
                           "cvxpy' or use a `lstsq` fitter instead of cvx_fit.")
@@ -118,7 +119,7 @@ def cvx_fit(data: np.array,
     # and imaginary parts of rho seperately: rho = rho_r + 1j * rho_i
 
     dim = int(np.sqrt(basis_matrix.shape[1]))
-    rho_r = cvxpy.Variable((dim, dim))
+    rho_r = cvxpy.Variable((dim, dim), symmetric=True)
     rho_i = cvxpy.Variable((dim, dim))
 
     # CONSTRAINTS
@@ -128,7 +129,7 @@ def cvx_fit(data: np.array,
     #   1. rho_r.T = rho_r.T  (real part is symmetric)
     #   2. rho_i.T = -rho_i.T  (imaginary part is anti-symmetric)
 
-    cons = [rho_r == rho_r.T, rho_i == -rho_i.T]
+    cons = [rho_i == -rho_i.T]
 
     # Trace constraint: note this should not be used at the same
     # time as the trace preserving constraint.
@@ -153,7 +154,7 @@ def cvx_fit(data: np.array,
     if trace_preserving is True:
         sdim = int(np.sqrt(dim))
         ptr = partial_trace_super(sdim, sdim)
-        cons.append(ptr * cvxpy.vec(rho_r) == np.identity(sdim).ravel())
+        cons.append(ptr @ cvxpy.vec(rho_r) == np.identity(sdim).ravel())
 
     # Rescale input data and matrix by weights if they are provided
     if weights is not None:
@@ -183,7 +184,7 @@ def cvx_fit(data: np.array,
         bm_r = bm_r.todense()
         bm_i = bm_i.todense()
 
-    arg = bm_r * cvxpy.vec(rho_r) - bm_i * cvxpy.vec(rho_i) - np.array(data)
+    arg = bm_r @ cvxpy.vec(rho_r) - bm_i @ cvxpy.vec(rho_i) - np.array(data)
 
     # SDP objective function
     obj = cvxpy.Minimize(cvxpy.norm(arg, p=2))
@@ -192,6 +193,10 @@ def cvx_fit(data: np.array,
     prob = cvxpy.Problem(obj, cons)
     iters = 5000
     max_iters = kwargs.get('max_iters', 20000)
+    # Set default solver if none is specified
+    if 'solver' not in kwargs:
+        if 'CVXOPT' in cvxpy.installed_solvers():
+            kwargs['solver'] = 'CVXOPT'
 
     problem_solved = False
     while not problem_solved: