qgd_Generative_Quantum_Machine_Learning_Base.py

Go to the documentation of this file.

"""

Created on Fri Jun 26 14:13:26 2020
Copyright 2020 Peter Rakyta, Ph.D.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

@author: Peter Rakyta, Ph.D.

"""




import numpy as np
from os import path
from squander.VQA.qgd_Generative_Quantum_Machine_Learning_Base_Wrapper import qgd_Generative_Quantum_Machine_Learning_Base_Wrapper
from squander.gates.qgd_Circuit import qgd_Circuit




class qgd_Generative_Quantum_Machine_Learning_Base(qgd_Generative_Quantum_Machine_Learning_Base_Wrapper):
    
    

    def __init__( self, x_bitstrings, p_stars, sigma, qbit_num, use_lookup_table, cliques, use_exact, config):
    
        # call the constructor of the wrapper class
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).__init__(x_bitstrings.data, p_stars.data, sigma, qbit_num, use_lookup_table, cliques, use_exact, config)
        self.qbit_num = qbit_num



    def set_Optimizer(self, alg):    

        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Optimizer(alg)


    def Start_Optimization(self):

     # call the C wrapper function
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).Start_Optimization()


    def get_Optimized_Parameters(self):
    
        return super(qgd_Generative_Quantum_Machine_Learning_Base, self).get_Optimized_Parameters()



    def set_Optimized_Parameters(self, new_params):
        
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Optimized_Parameters(new_params)



# TODO should be deleted!        
    def set_Optimization_Tolerance(self, tolerance):
    
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Optimization_Tolerance(tolerance)



    def set_Project_Name(self, project_name):
    
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Project_Name(project_name)


    def set_Gate_Structure_from_Binary(self, filename):
    
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Gate_Structure_From_Binary(filename)



    def set_Ansatz(self, ansatz_new):
        
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Ansatz(ansatz_new)
        

    def Generate_Circuit(self, layers, inner_blocks=1):
    
        super(qgd_Generative_Quantum_Machine_Learning_Base, self).Generate_Circuit( layers, inner_blocks )
        

    def Optimization_Problem(self, parameters):
    
        return super(qgd_Generative_Quantum_Machine_Learning_Base, self).Optimization_Problem(parameters)



    def get_Second_Renyi_Entropy(self, parameters=None, input_state=None, qubit_list=None ):

        qbit_num = self.get_Qbit_Num()

        qubit_list_validated = list()
        if isinstance(qubit_list, list) or isinstance(qubit_list, tuple):
            for item in qubit_list:
                if isinstance(item, int):
                    qubit_list_validated.append(item)
                    qubit_list_validated = list(set(qubit_list_validated))
                else:
                    print("Elements of qbit_list should be integers")
                    return
        elif qubit_list == None:
            qubit_list_validated = [ x for x in range(qbit_num) ]

        else:
            print("Elements of qbit_list should be integers")
            return
        

        if parameters is None:
            print( "get_Second_Renyi_entropy: array of input parameters is None")
            return None


        if input_state is None:
            matrix_size = 1 << qbit_num
            input_state = np.zeros( (matrix_size,1) )
            input_state[0] = 1

        # evaluate the entropy
        entropy = super(qgd_Generative_Quantum_Machine_Learning_Base, self).get_Second_Renyi_Entropy( parameters, input_state, qubit_list_validated)  


        return entropy



    def get_Qbit_Num(self):
    
        return super(qgd_Generative_Quantum_Machine_Learning_Base, self).get_Qbit_Num()



    def get_Parameter_Num( self ):

        return super(qgd_Generative_Quantum_Machine_Learning_Base, self).get_Parameter_Num()
        
        

#@brief Call to apply the gate operation on the input state
#@param parameters_mtx Python array ontaining the parameter set
#@param state_to_be_transformed Numpy array storing the state on which the transformation should be applied
    def apply_to( self, parameters_mtx, state_to_be_transformed):

     # call the C wrapper function
        super().apply_to( parameters_mtx, state_to_be_transformed )



    def get_Circuit( self ):
        
        # call the C wrapper function
        return super().get_Circuit()




    def get_Qiskit_Circuit(self):

        from squander import Qiskit_IO
        
        squander_circuit = self.get_Circuit()
        parameters       = self.get_Optimized_Parameters()
        
        return Qiskit_IO.get_Qiskit_Circuit( squander_circuit, parameters )



    def set_Initial_State( self, initial_state ):

        super(qgd_Generative_Quantum_Machine_Learning_Base, self).set_Initial_State( initial_state )



    def set_Gate_Structure( self, Gate_structure ):  

        if not isinstance(Gate_structure, qgd_Circuit) :
            raise Exception("Input parameter Gate_structure should be a an instance of Circuit")
                    
                    
        return super().set_Gate_Structure( Gate_structure )