Generative_Quantum_Machine_Learning_Base.h

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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.
*/
#ifndef GENERATIVE_QUANTUM_MACHINE_LEARNING_BASE_H
#define GENERATIVE_QUANTUM_MACHINE_LEARNING_BASE_H

#include "Optimization_Interface.h"
#include "matrix_real.h"

typedef enum ansatz_type {HEA, HEA_ZYZ, QCMRF} ansatz_type;
    


class Generative_Quantum_Machine_Learning_Base : public Optimization_Interface {
public:


private:

    std::vector<int> sample_indices;

    std::vector<std::vector<int>> sample_bitstrings;

    Matrix_real P_star;
    
    Matrix initial_state;
    
    ansatz_type ansatz;

    double ev_P_star_P_star;

    Matrix_real sigma;
    
    // Lookup table for the gauss kernels
    std::vector<std::vector<double>> gaussian_lookup_table;

    // Decide to use lookup table
    bool use_lookup;

    std::vector<std::vector<int>> all_bitstrings;

    // Number samples
    int sample_size;

    // The cliques in the graph
    std::vector<std::vector<int>> cliques;

    double (Generative_Quantum_Machine_Learning_Base::*MMD_of_the_distributions)(Matrix&);

    bool use_exact;

public:


Generative_Quantum_Machine_Learning_Base();


Generative_Quantum_Machine_Learning_Base( std::vector<int> sample_indices_in, std::vector<std::vector<int>> sample_bitstrings_in, Matrix_real P_star_in, Matrix_real sigma_in, int qbit_num_in, bool use_lookup_table_in, std::vector<std::vector<int>> cliques_in, bool use_exact, std::map<std::string, Config_Element>& config_in);

virtual ~Generative_Quantum_Machine_Learning_Base();


double Gaussian_kernel(int x, int y, Matrix_real& sigma);

double expectation_value_P_star_P_star_approx();

double expectation_value_P_star_P_star_exact();

void fill_lookup_table();

double TV_of_the_distributions(Matrix& State_right);

double MMD_of_the_distributions_approx(Matrix& State_right);

double MMD_of_the_distributions_exact(Matrix& State_right);


virtual double optimization_problem(Matrix_real& parameters) override;


virtual double optimization_problem_non_static( Matrix_real parameters, void* void_instance) override;


using Decomposition_Base::optimization_problem;

double optimization_problem( double* parameters);


virtual void optimization_problem_combined_non_static( Matrix_real parameters, void* void_instance, double* f0, Matrix_real& grad ) override;


virtual void optimization_problem_combined( Matrix_real parameters, double* f0, Matrix_real grad ) override;


static void optimization_problem_grad_vqe( Matrix_real parameters, void* void_instance, Matrix_real& grad );

void export_current_cost_fnc(double current_minimum, Matrix_real& parameters) override;

void initialize_zero_state();


void start_optimization();


void set_ansatz(ansatz_type ansatz_in);


void generate_circuit( int layers, int inner_blocks );


void generate_clique_circuit(int i, std::vector<int>& qbits, std::vector<std::vector<int>>& res, std::vector<int>& subset);


void MultyRZ(std::vector<int>& qbits);

void set_gate_structure( std::string filename );


void set_initial_state( Matrix initial_state_in );



};

#endif