def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
# build the feature map
def self_product(x: np.ndarray) -> float:
"""
Define a function map from R^n to R.
Args:
x: data
Returns:
float: the mapped value
"""
coeff = x[0] if len(x) == 1 else \
functools.reduce(lambda m, n:np.square( m * n), np.pi - x)
return coeff
feature_dim = 5 # equal to the dimension of the data
feature_map = ZFeatureMap(feature_dimension=feature_dim,
data_map_func=self_product,
reps=2)
feature_map.draw()
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map # the write_and_run function writes the content in this cell into the file "variational_circuit.py"
def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
# build the feature map
feature_dim = 3 # equal to the dimension of the data
feature_map = ZFeatureMap(feature_dimension=feature_dim, reps=4)
feature_map.draw()
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map
def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
# build the feature map
feature_dim = 3 # equal to the dimension of the data
feature_map = ZFeatureMap(feature_dimension=feature_dim, reps=2)
feature_map.draw()
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map # the write_and_run function writes the content in this cell into the file "variational_circuit.py"