Merge branch 'PJ-devel' of https://github.com/Praveen91299/tequila in…

…to PJ-devel

.github/workflows/ci_pyquil.yml

@@ -29,8 +29,9 @@ jobs:
         python -m pip install --upgrade pip
         pip install --upgrade pytest
         pip install -r requirements.txt
-        pip install "pyquil<3.0"
+        pip install "pyquil<3.0" # needs updates
         pip install -e .
+        pip install --upgrade pip 'urllib3<2' # issues with old pyquil version otherwise
         docker pull rigetti/qvm:edge
         docker pull rigetti/quilc
         docker run --rm -itd -p 5555:5555 rigetti/quilc -R

README.md

@@ -15,7 +15,7 @@ Tequila can execute the underlying quantum expectation values on state of the ar
 
 # Installation
 Recommended Python version is 3.8-3.9.   
-Tequila supports linux, osx and windows. However, not all optional dependencies are supported on windows.
+Tequila supports linux, osx and windows. However, not all optional dependencies are supported on windows.  
 
 ## Install from PyPi
 **Do not** install like this: (Minecraft lovers excluded)

requirements.txt

@@ -20,7 +20,6 @@ qulacs # default simulator (best integration), remove if the installation gives
 #qibo <= 0.1.1 # can not be installed in the same environment as gpyopt
 
 #optional optimizers
-#phoenics # version on PyPi  isc urrently broken, we recommend to install from source (AAG github)
 #gpyopt # not in combination with qibo as quantum backend
 
 #optional third party libraries

src/tequila/apps/adapt/adapt.py

@@ -21,7 +21,7 @@ class AdaptParameters:
     degeneracy_threshold: float = 5.e-4
     silent: bool = False
 
-    def __post__init__(self):
+    def __post_init__(self):
         # avoid stacking of same operator-types in a row
         if "method_options" in self.optimizer_args:
             if "gtol" in self.optimizer_args["method_options"]:
@@ -150,6 +150,10 @@ def __call__(self, static_variables = None, mp_pool=None, label=None, variables=
             variables = {**variables, **static_variables}
 
         U = QCircuit()
+        if "U" in kwargs:
+            U = kwargs["U"]
+        elif hasattr(self.operator_pool, "initialize_circuit"):
+            U = self.operator_pool.initialize_circuit()
 
         initial_objective = self.make_objective(U, variables = variables)
         for k in initial_objective.extract_variables():

src/tequila/circuit/gates.py

@@ -1081,10 +1081,14 @@ def map_qubits(self, qubit_map: dict):
     def compile(self, exponential_pauli=False, *args, **kwargs):
         # optimized compiling for single and double qubit excitaitons following arxiv:2005.14475
         # Alternative representation in arxiv:2104.05695 (not implemented -> could be added and controlled with optional compile keywords)
+        if self.is_controlled():
+            control = list(self.control)
+        else:
+            control = []
         if self.compile_options == "optimize" and len(self.target) == 2 and exponential_pauli:
             p,q = self.target
             U0 = X(target=p, control=q)
-            U1 = Ry(angle=self.parameter, target=q, control=p)
+            U1 = Ry(angle=self.parameter, target=q, control=[p]+control)
             return U0 + U1 + U0
         elif self.compile_options == "optimize" and len(self.target) == 4 and exponential_pauli:
             p,r,q,s = self.target
@@ -1093,10 +1097,11 @@ def compile(self, exponential_pauli=False, *args, **kwargs):
             U0 += X(target=r, control=p)
             U0 += X(target=q)
             U0 += X(target=s)
-            U1 = Ry(angle=-self.parameter, target=p, control=[q,r,s])
+            U1 = Ry(angle=-self.parameter, target=p, control=[q,r,s]+control)
             return U0 + U1 + U0.dagger()
         else:
-            return Trotterized(angle=self.parameter, generator=self.generator, steps=1)
+            return Trotterized(angle=self.parameter, generator=self.generator, steps=1, control=self.control)
+
 
 def _convert_Paulistring(paulistring: typing.Union[PauliString, str, dict]) -> PauliString:
     '''

src/tequila/optimizers/__init__.py

@@ -16,7 +16,7 @@ class _Optimizers:
     methods: list = None
 
 
-SUPPORTED_OPTIMIZERS = ['scipy', 'phoenics', 'gpyopt', 'gd']
+SUPPORTED_OPTIMIZERS = ['scipy', 'gpyopt', 'gd']
 INSTALLED_OPTIMIZERS = {}
 INSTALLED_OPTIMIZERS['scipy'] = _Optimizers(cls=OptimizerSciPy,
                                             minimize=minimize_scipy,
@@ -37,19 +37,6 @@ class _Optimizers:
 except ImportError:
     has_gpyopt = False
 
-has_phoenics = False
-try:
-    from tequila.optimizers.optimizer_phoenics import OptimizerPhoenics
-    from tequila.optimizers.optimizer_phoenics import minimize as minimize_phoenics
-
-    INSTALLED_OPTIMIZERS['phoenics'] = _Optimizers(cls=OptimizerPhoenics,
-                                                   minimize=minimize_phoenics,
-                                                   methods=OptimizerPhoenics.available_methods())
-    has_phoenics = True
-except ImportError:
-    has_phoenics = False
-
-
 def show_available_optimizers(module=None):
     """
     Returns

src/tequila/optimizers/optimizer_gd.py

@@ -334,11 +334,12 @@ def __call__(self, objective: Objective,
 
 
             if not self.silent:
+                self.__dx = numpy.asarray(self.__dx)
                 print("%3i   %+15.8f   %+7.2e   %7.3e   %7.3e    %s"
                       % (step,
                          e,
                          e-last,
-                         numpy.max(abs(self.__dx)),
+                         numpy.max([abs(x) for x in self.__dx]),
                          numpy.sqrt(numpy.average(self.__dx**2)),
                          comment))
 

src/tequila/optimizers/optimizer_gpyopt.py

@@ -4,8 +4,6 @@
 import numbers
 from tequila.objective.objective import Variable
 import warnings
-
-warnings.simplefilter("ignore")
 import GPyOpt
 from GPyOpt.methods import BayesianOptimization
 import numpy as np