Optimise import qiskit with lazy imports

qiskit/algorithms/optimizers/bobyqa.py

@@ -15,16 +15,9 @@
 from typing import Any, Dict, Tuple, List, Callable, Optional
 
 import numpy as np
-from qiskit.exceptions import MissingOptionalLibraryError
+from qiskit.utils import optionals as _optionals
 from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
 
-try:
-    import skquant.opt as skq
-
-    _HAS_SKQUANT = True
-except ImportError:
-    _HAS_SKQUANT = False
-
 
 class BOBYQA(Optimizer):
     """Bound Optimization BY Quadratic Approximation algorithm.
@@ -48,10 +41,7 @@ def __init__(
         Raises:
             MissingOptionalLibraryError: scikit-quant not installed
         """
-        if not _HAS_SKQUANT:
-            raise MissingOptionalLibraryError(
-                libname="scikit-quant", name="BOBYQA", pip_install="pip install scikit-quant"
-            )
+        _optionals.HAS_SKQUANT.require_now("BOBYQA")
         super().__init__()
         self._maxiter = maxiter
 
@@ -74,6 +64,8 @@ def minimize(
         jac: Optional[Callable[[POINT], POINT]] = None,
         bounds: Optional[List[Tuple[float, float]]] = None,
     ) -> OptimizerResult:
+        from skquant import opt as skq
+
         res, history = skq.minimize(
             func=fun,
             x0=np.asarray(x0),

qiskit/algorithms/optimizers/imfil.py

@@ -14,16 +14,9 @@
 
 from typing import Any, Dict, Callable, Optional, List, Tuple
 
-from qiskit.exceptions import MissingOptionalLibraryError
+from qiskit.utils import optionals as _optionals
 from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
 
-try:
-    import skquant.opt as skq
-
-    _HAS_SKQUANT = True
-except ImportError:
-    _HAS_SKQUANT = False
-
 
 class IMFIL(Optimizer):
     """IMplicit FILtering algorithm.
@@ -49,10 +42,7 @@ def __init__(
         Raises:
             MissingOptionalLibraryError: scikit-quant not installed
         """
-        if not _HAS_SKQUANT:
-            raise MissingOptionalLibraryError(
-                libname="scikit-quant", name="IMFIL", pip_install="pip install scikit-quant"
-            )
+        _optionals.HAS_SKQUANT.require_now("IMFIL")
         super().__init__()
         self._maxiter = maxiter
 
@@ -77,6 +67,8 @@ def minimize(
         jac: Optional[Callable[[POINT], POINT]] = None,
         bounds: Optional[List[Tuple[float, float]]] = None,
     ) -> OptimizerResult:
+        from skquant import opt as skq
+
         res, history = skq.minimize(
             func=fun,
             x0=x0,

qiskit/algorithms/optimizers/nlopts/nloptimizer.py

@@ -17,24 +17,12 @@
 from abc import abstractmethod
 import logging
 import numpy as np
-from qiskit.exceptions import MissingOptionalLibraryError
+
+from qiskit.utils import optionals as _optionals
 from ..optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
 
 logger = logging.getLogger(__name__)
 
-try:
-    import nlopt
-
-    logger.info(
-        "NLopt version: %s.%s.%s",
-        nlopt.version_major(),
-        nlopt.version_minor(),
-        nlopt.version_bugfix(),
-    )
-    _HAS_NLOPT = True
-except ImportError:
-    _HAS_NLOPT = False
-
 
 class NLoptOptimizerType(Enum):
     """NLopt Valid Optimizer"""
@@ -51,6 +39,8 @@ class NLoptOptimizer(Optimizer):
     NLopt global optimizer base class
     """
 
+    # pylint: disable=import-error
+
     _OPTIONS = ["max_evals"]
 
     def __init__(self, max_evals: int = 1000) -> None:  # pylint: disable=unused-argument
@@ -61,15 +51,8 @@ def __init__(self, max_evals: int = 1000) -> None:  # pylint: disable=unused-arg
         Raises:
             MissingOptionalLibraryError: NLopt library not installed.
         """
-        if not _HAS_NLOPT:
-            raise MissingOptionalLibraryError(
-                libname="nlopt",
-                name="NLoptOptimizer",
-                msg=(
-                    "See https://qiskit.org/documentation/apidoc/"
-                    "qiskit.algorithms.optimizers.nlopts.html for installation information"
-                ),
-            )
+        _optionals.HAS_NLOPT.require_now("NLoptOptimizer")
+        import nlopt
 
         super().__init__()
         for k, v in list(locals().items()):
@@ -124,6 +107,8 @@ def minimize(
         jac: Optional[Callable[[POINT], POINT]] = None,
         bounds: Optional[List[Tuple[float, float]]] = None,
     ) -> OptimizerResult:
+        import nlopt
+
         x0 = np.asarray(x0)
 
         if bounds is None:

qiskit/algorithms/optimizers/snobfit.py

@@ -15,25 +15,10 @@
 from typing import Any, Dict, Optional, Callable, Tuple, List
 
 import numpy as np
-from qiskit.exceptions import MissingOptionalLibraryError
+from qiskit.utils import optionals as _optionals
 from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
 
 
-try:
-    import skquant.opt as skq
-
-    _HAS_SKQUANT = True
-except ImportError:
-    _HAS_SKQUANT = False
-
-try:
-    from SQSnobFit import optset
-
-    _HAS_SKSNOBFIT = True
-except ImportError:
-    _HAS_SKSNOBFIT = False
-
-
 class SNOBFIT(Optimizer):
     """Stable Noisy Optimization by Branch and FIT algorithm.
 
@@ -64,14 +49,8 @@ def __init__(
         Raises:
             MissingOptionalLibraryError: scikit-quant or SQSnobFit not installed
         """
-        if not _HAS_SKQUANT:
-            raise MissingOptionalLibraryError(
-                libname="scikit-quant", name="SNOBFIT", pip_install="pip install scikit-quant"
-            )
-        if not _HAS_SKSNOBFIT:
-            raise MissingOptionalLibraryError(
-                libname="SQSnobFit", name="SNOBFIT", pip_install="pip install SQSnobFit"
-            )
+        _optionals.HAS_SKQUANT.require_now("SNOBFIT")
+        _optionals.HAS_SQSNOBFIT.require_now("SNOBFIT")
         super().__init__()
         self._maxiter = maxiter
         self._maxfail = maxfail
@@ -102,6 +81,9 @@ def minimize(
         jac: Optional[Callable[[POINT], POINT]] = None,
         bounds: Optional[List[Tuple[float, float]]] = None,
     ) -> OptimizerResult:
+        import skquant.opt as skq
+        from SQSnobFit import optset
+
         snobfit_settings = {
             "maxmp": self._maxmp,
             "maxfail": self._maxfail,

qiskit/circuit/gate.py

@@ -15,7 +15,6 @@
 from warnings import warn
 from typing import List, Optional, Union, Tuple
 import numpy as np
-from scipy.linalg import schur
 
 from qiskit.circuit.parameterexpression import ParameterExpression
 from qiskit.circuit.exceptions import CircuitError
@@ -72,6 +71,7 @@ def power(self, exponent: float):
         """
         from qiskit.quantum_info.operators import Operator  # pylint: disable=cyclic-import
         from qiskit.extensions.unitary import UnitaryGate  # pylint: disable=cyclic-import
+        from scipy.linalg import schur
 
         # Should be diagonalized because it's a unitary.
         decomposition, unitary = schur(Operator(self).data, output="complex")

qiskit/circuit/parameter.py

@@ -15,15 +15,9 @@
 
 from uuid import uuid4
 
+from qiskit.utils import optionals as _optionals
 from .parameterexpression import ParameterExpression
 
-try:
-    import symengine
-
-    HAS_SYMENGINE = True
-except ImportError:
-    HAS_SYMENGINE = False
-
 
 class Parameter(ParameterExpression):
     """Parameter Class for variable parameters.
@@ -81,11 +75,13 @@ def __init__(self, name: str):
                 be any unicode string, e.g. "ϕ".
         """
         self._name = name
-        if not HAS_SYMENGINE:
+        if not _optionals.HAS_SYMENGINE:
             from sympy import Symbol
 
             symbol = Symbol(name)
         else:
+            import symengine
+
             symbol = symengine.Symbol(name)
         super().__init__(symbol_map={self: symbol}, expr=symbol)
 
@@ -126,10 +122,12 @@ def __getstate__(self):
 
     def __setstate__(self, state):
         self._name = state["name"]
-        if not HAS_SYMENGINE:
+        if not _optionals.HAS_SYMENGINE:
             from sympy import Symbol
 
             symbol = Symbol(self._name)
         else:
+            import symengine
+
             symbol = symengine.Symbol(self._name)
         super().__init__(symbol_map={self: symbol}, expr=symbol)