qgd_Variational_Quantum_Eigensolver_Base.py

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"""

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_Variational_Quantum_Eigensolver_Base_Wrapper import qgd_Variational_Quantum_Eigensolver_Base_Wrapper
from squander.gates.qgd_Circuit import qgd_Circuit

_VQE_BACKEND_NAME_TO_CODE = {
    "state_vector": 0,
    "density_matrix": 1,
}
_VQE_DEFAULT_BACKEND = "state_vector"
_VQE_BACKEND_CONFIG_KEY = "backend_mode"
_DENSITY_NOISE_CHANNEL_SPECS = {
    "depolarizing": ("local_depolarizing", "error_rate"),
    "local_depolarizing": ("local_depolarizing", "error_rate"),
    "amplitude_damping": ("amplitude_damping", "gamma"),
    "phase_damping": ("phase_damping", "lambda"),
    "dephasing": ("phase_damping", "lambda"),
}


def _normalize_vqe_backend_name(backend):

    if backend is None:
        return _VQE_DEFAULT_BACKEND

    if not isinstance(backend, str):
        raise TypeError(
            "backend should be one of 'state_vector', 'density_matrix', or None"
        )

    normalized_backend = backend.strip()
    if normalized_backend not in _VQE_BACKEND_NAME_TO_CODE:
        raise ValueError(
            "Unsupported backend '{}'. Supported backends are 'state_vector' and "
            "'density_matrix'.".format(backend)
        )

    return normalized_backend


def _normalize_density_noise_spec(density_noise):

    if density_noise is None:
        return []

    if not isinstance(density_noise, (list, tuple)):
        raise TypeError("density_noise should be a list or tuple of dictionaries")

    normalized_density_noise = []
    for item_idx, noise_item in enumerate(density_noise):
        if not isinstance(noise_item, dict):
            raise TypeError(
                "density_noise[{}] should be a dictionary".format(item_idx)
            )

        channel = noise_item.get("channel", noise_item.get("type"))
        if not isinstance(channel, str):
            raise TypeError(
                "density_noise[{}] should define a string 'channel'".format(
                    item_idx
                )
            )

        channel_name = channel.strip()
        if channel_name not in _DENSITY_NOISE_CHANNEL_SPECS:
            raise ValueError(
                "Unsupported density-noise channel '{}'. Supported channels are "
                "'local_depolarizing', 'amplitude_damping', and "
                "'phase_damping'.".format(channel)
            )

        canonical_channel, value_key = _DENSITY_NOISE_CHANNEL_SPECS[channel_name]
        raw_value = noise_item.get("value", noise_item.get(value_key))
        if canonical_channel == "phase_damping" and raw_value is None:
            raw_value = noise_item.get("lambda_param")

        if raw_value is None:
            raise ValueError(
                "density_noise[{}] is missing the '{}' value".format(
                    item_idx, value_key
                )
            )

        target = noise_item.get("target")
        after_gate_index = noise_item.get("after_gate_index")
        if isinstance(target, bool) or not isinstance(target, int):
            raise TypeError(
                "density_noise[{}].target should be an integer".format(item_idx)
            )
        if isinstance(after_gate_index, bool) or not isinstance(after_gate_index, int):
            raise TypeError(
                "density_noise[{}].after_gate_index should be an integer".format(
                    item_idx
                )
            )

        value = float(raw_value)
        if not np.isfinite(value):
            raise ValueError(
                "density_noise[{}] has a non-finite value".format(item_idx)
            )

        normalized_density_noise.append(
            {
                "channel": canonical_channel,
                "target": int(target),
                "after_gate_index": int(after_gate_index),
                "value": value,
            }
        )

    return normalized_density_noise




class qgd_Variational_Quantum_Eigensolver_Base(qgd_Variational_Quantum_Eigensolver_Base_Wrapper):
    
    

    def __init__(
        self,
        Hamiltonian,
        qbit_num,
        config=None,
        accelerator_num=0,
        *,
        backend=None,
        density_noise=None,
    ):
    

        if config is None:
            config = {}

        # config
        if not isinstance(config, dict):
            print("Input parameter config should be a dictionary describing the following hyperparameters:") #TODO
            return

        normalized_backend = _normalize_vqe_backend_name(backend)
        config_copy = dict(config)
        config_copy[_VQE_BACKEND_CONFIG_KEY] = _VQE_BACKEND_NAME_TO_CODE[normalized_backend]

        # call the constructor of the wrapper class
        super(qgd_Variational_Quantum_Eigensolver_Base, self).__init__(Hamiltonian.data, Hamiltonian.indices, Hamiltonian.indptr, qbit_num, config=config_copy, accelerator_num=accelerator_num)
        self.qbit_num = qbit_num
        # Keep the selected backend visible on the Python object so tests,
        # validation harnesses, and reproducibility artifacts can attribute
        # which execution path was requested.
        self.backend = normalized_backend
        self.density_noise = []
        self.set_Density_Matrix_Noise(density_noise)



    def set_Optimizer(self, alg):    

        super(qgd_Variational_Quantum_Eigensolver_Base, self).set_Optimizer(alg)


    def Start_Optimization(self):

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


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



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



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



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


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



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

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

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


    def Optimization_Problem_Grad(self, parameters):
    
        return super(qgd_Variational_Quantum_Eigensolver_Base, self).Optimization_Problem_Grad(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_Variational_Quantum_Eigensolver_Base, self).get_Second_Renyi_Entropy( parameters, input_state, qubit_list_validated)  


        return entropy



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



    def get_Parameter_Num( self ):

        return super(qgd_Variational_Quantum_Eigensolver_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_Variational_Quantum_Eigensolver_Base, self).set_Initial_State( initial_state )



    def set_Density_Matrix_Noise(self, density_noise):

        normalized_density_noise = _normalize_density_noise_spec(density_noise)
        super(qgd_Variational_Quantum_Eigensolver_Base, self).set_Density_Matrix_Noise(
            normalized_density_noise
        )
        self.density_noise = [dict(item) for item in normalized_density_noise]



    def describe_density_bridge(self):

        bridge_metadata = super(
            qgd_Variational_Quantum_Eigensolver_Base, self
        ).get_Density_Matrix_Bridge_Metadata()
        bridge_metadata["density_noise"] = [dict(item) for item in self.density_noise]
        return bridge_metadata



    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 )