Commit db79b550 authored by Dmitry I. Lyakh's avatar Dmitry I. Lyakh
Browse files

Updated transverse Ising test with excited states. 



Signed-off-by: default avatarDmitry I. Lyakh <quant4me@gmail.com>
parent 0d43cb54

src/exatn/tests/NumServerTester.cpp

+46 −17
Original line number Diff line number Diff line
@@ -47,9 +47,9 @@ 
#define EXATN_TEST26

//#define EXATN_TEST27 //requires input file from source

//#define EXATN_TEST28 //requires input file from source

#define EXATN_TEST29

#define EXATN_TEST30*/

#define EXATN_TEST31 //requires input file from source

#define EXATN_TEST29*/

#define EXATN_TEST30

//#define EXATN_TEST31 //requires input file from source

//#define EXATN_TEST32
@@ -3414,10 +3414,13 @@ TEST(NumServerTester, SpinHamiltonians) { 
 const int max_bond_dim = std::min(static_cast<int>(std::pow(2,num_spin_sites/2)),bond_dim_lim);

 const int arity = 2;

 const std::string tn_type = "TTN"; //MPS or TTN

 const unsigned int num_states = 8;

 const double accuracy = 1e-4;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;

 bool root = (exatn::getProcessRank() == 0);



 //Define the 1D Ising Hamiltonian generator:

 TensorRange spin_sites({num_spin_sites});
@@ -3474,6 +3477,10 @@ TEST(NumServerTester, SpinHamiltonians) { 
                                                                 TENS_ELEM_TYPE);

 //transverse_field->printIt(); //debug



 //Construct the full transverse field Ising Hamiltonian:

 auto transverse_ising = exatn::combineTensorOperators(*ising_hamiltonian0,*transverse_field);

 //transverse_ising->printIt(); //debug



 //Configure the tensor network builder:

 auto tn_builder = exatn::getTensorNetworkBuilder(tn_type);

 if(tn_type == "MPS"){
@@ -3502,7 +3509,7 @@ TEST(NumServerTester, SpinHamiltonians) { 
 //Numerical processing:

 {

  //Create and initialize tensor network vector tensors:

  std::cout << "Creating and initializing tensor network vector tensors ... ";

  if(root) std::cout << "Creating and initializing tensor network vector tensors ... ";

  success = exatn::createTensorsSync(*vec_net0,TENS_ELEM_TYPE); assert(success);

  success = exatn::initTensorsRndSync(*vec_net0); assert(success);

  success = exatn::createTensorsSync(*vec_net1,TENS_ELEM_TYPE); assert(success);
@@ -3511,8 +3518,28 @@ TEST(NumServerTester, SpinHamiltonians) { 
  success = exatn::initTensorsRndSync(*vec_net2); assert(success);

  success = exatn::createTensorsSync(*rhs_net,TENS_ELEM_TYPE); assert(success);

  success = exatn::initTensorsRndSync(*rhs_net); assert(success);

  std::cout << "Ok" << std::endl;

  if(root) std::cout << "Ok" << std::endl;



  //Ground and three excited states in one call:

  if(root) std::cout << "Ground and excited states search for the original Hamiltonian:" << std::endl;

  exatn::TensorNetworkOptimizer::resetDebugLevel(1,0);

  exatn::TensorNetworkOptimizer optimizer(transverse_ising,vec_tns0,accuracy);

  success = exatn::sync(); assert(success);

  bool converged = optimizer.optimize(num_states);

  success = exatn::sync(); assert(success);

  if(converged){

   if(root){

    std::cout << "Search succeeded:" << std::endl;

    for(unsigned int root_id = 0; root_id < num_states; ++root_id){

     std::cout << "Expectation value " << root_id << " = "

               << optimizer.getExpectationValue(root_id) << std::endl;

    }

   }

  }else{

   if(root) std::cout << "Search failed!" << std::endl;

   assert(false);

  }

#if 0

  //Ground state search for the original Hamiltonian:

  std::cout << "Ground state search for the original Hamiltonian:" << std::endl;

  exatn::TensorNetworkOptimizer::resetDebugLevel(1,0);
@@ -3574,6 +3601,7 @@ TEST(NumServerTester, SpinHamiltonians) { 
  }

  const auto expect_val2 = optimizer2.getExpectationValue();

  std::cout << "Expectation value = " << expect_val2 << std::endl;

#endif

 }



 //Synchronize:
@@ -3605,6 +3633,7 @@ TEST(NumServerTester, ExcitedMCVQE) { 
 const int max_bond_dim = std::min(static_cast<int>(std::pow(2,num_spin_sites/2)),bond_dim_lim);

 const int arity = 2;

 const std::string tn_type = "TTN"; //MPS or TTN

 const unsigned int num_states = 3;

 const double accuracy = 1e-4;



 //exatn::resetLoggingLevel(1,2); //debug
@@ -3713,27 +3742,27 @@ TEST(NumServerTester, ExcitedMCVQE) { 
  if(root) std::cout << "Expectation value = " << expect_val2 << std::endl;

#endif

  //Ground and three excited states in one call:

  if(root) std::cout << "Ground and three excited states search for the original Hamiltonian:" << std::endl;

  if(root) std::cout << "Ground and excited states search for the original Hamiltonian:" << std::endl;

  exatn::TensorNetworkOptimizer::resetDebugLevel(1,0);

  vec_net0->markOptimizableAllTensors();

  success = exatn::initTensorsRndSync(*vec_tns0); assert(success);

  exatn::TensorNetworkOptimizer optimizer3(hamiltonian0,vec_tns0,accuracy);

  success = exatn::sync(); assert(success);

  bool converged = optimizer3.optimize(4);

  bool converged = optimizer3.optimize(num_states);

  success = exatn::sync(); assert(success);

  if(exatn::getProcessRank() == 0){

  if(converged){

    if(root) std::cout << "Search succeeded:" << std::endl;

    for(unsigned int root_id = 0; root_id < 4; ++root_id){

     if(root) std::cout << "Expectation value " << root_id << " = "

   if(root){

    std::cout << "Search succeeded:" << std::endl;

    for(unsigned int root_id = 0; root_id < num_states; ++root_id){

     std::cout << "Expectation value " << root_id << " = "

               << optimizer3.getExpectationValue(root_id) << std::endl;

    }

   }

  }else{

   if(root) std::cout << "Search failed!" << std::endl;

   assert(false);

  }

 }

 }



 //Synchronize:

 success = exatn::sync(); assert(success);