Added systematic MC-VQE ground state tests

Signed-off-by: Dmitry I. Lyakh <quant4me@gmail.com>

src/exatn/optimizer.cpp

 /** ExaTN:: Variational optimizer of a closed symmetric tensor network expansion functional
-REVISION: 2021/09/17
+REVISION: 2021/09/25
 
 Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
 Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -226,12 +226,12 @@ bool TensorNetworkOptimizer::optimize(const ProcessGroup & process_group)
      if(TensorNetworkOptimizer::debug > 1) std::cout << " Operator expectation value w.r.t. " << environment.tensor->getName()
                                                      << " = " << std::scientific << expect_val << std::endl;
      //Update the expectation value in the gradient expansion:
-     if(TensorNetworkOptimizer::debug > 1){
+     if(TensorNetworkOptimizer::debug > 2){
       std::cout << " Old gradient expansion coefficients:\n";
       environment.gradient_expansion.printCoefficients();
      }
      scale_metrics(environment.gradient_expansion,environment.expect_value,expect_val);
-     if(TensorNetworkOptimizer::debug > 1){
+     if(TensorNetworkOptimizer::debug > 2){
       std::cout << " New gradient expansion coefficients:\n";
       environment.gradient_expansion.printCoefficients();
      }
@@ -268,12 +268,12 @@ bool TensorNetworkOptimizer::optimize(const ProcessGroup & process_group)
       if(micro_iteration == (micro_iterations_ - 1)) converged = false;
      }
       //Compute the optimal step size:
-      if(TensorNetworkOptimizer::debug > 1){
+      if(TensorNetworkOptimizer::debug > 2){
        std::cout << " Old hessian expansion coefficients:\n";
        environment.hessian_expansion.printCoefficients();
       }
       scale_metrics(environment.hessian_expansion,environment.expect_value,expect_val);
-      if(TensorNetworkOptimizer::debug > 1){
+      if(TensorNetworkOptimizer::debug > 2){
        std::cout << " New hessian expansion coefficients:\n";
        environment.hessian_expansion.printCoefficients();
       }

src/exatn/tests/NumServerTester.cpp

@@ -18,7 +18,7 @@
 #include "errors.hpp"
 
 //Test activation:
-#define EXATN_TEST0
+/*#define EXATN_TEST0
 #define EXATN_TEST1
 #define EXATN_TEST2
 #define EXATN_TEST3
@@ -44,9 +44,10 @@
 #define EXATN_TEST23
 #define EXATN_TEST24
 #define EXATN_TEST25
-//#define EXATN_TEST26 //requires input file from source
-#define EXATN_TEST27
-#define EXATN_TEST28
+#define EXATN_TEST26*/
+//#define EXATN_TEST27 //requires input file from source
+#define EXATN_TEST28 //requires input file from source
+//#define EXATN_TEST30
 
 
 #ifdef EXATN_TEST0
@@ -2853,6 +2854,148 @@ TEST(NumServerTester, OptimizerHubbard) {
 #endif
 
 #ifdef EXATN_TEST26
+TEST(NumServerTester, ExaTNGenVisitor) {
+ using exatn::TensorShape;
+ using exatn::TensorSignature;
+ using exatn::Tensor;
+ using exatn::TensorComposite;
+ using exatn::TensorNetwork;
+ using exatn::TensorExpansion;
+ using exatn::TensorOperator;
+ using exatn::TensorElementType;
+
+ const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX64;
+
+ //Test configuration:
+ const int num_sites = 16, max_bond_dim = std::min(static_cast<int>(std::pow(2,num_sites/2)),1);
+ const int max_layers = (num_sites - 1); //1 less CNOT gates
+ bool EVALUATE_FULL_TENSOR = false;
+
+ //exatn::resetLoggingLevel(2,2); //debug
+
+ bool success = true;
+
+ //Define the initial qubit state vector:
+ std::vector<std::complex<double>> qzero {
+  {1.0,0.0}, {0.0,0.0}
+ };
+
+ //Create a shallow quantum circuit:
+ // Create and initialize qubit tensors:
+ for(unsigned int i = 0; i < num_sites; ++i){
+  success = exatn::createTensor("Q"+std::to_string(i),TENS_ELEM_TYPE,TensorShape{2}); assert(success);
+ }
+ for(unsigned int i = 0; i < num_sites; ++i){
+  success = exatn::initTensorData("Q"+std::to_string(i),qzero); assert(success);
+ }
+ // Create and initalize 1-body gates:
+ success = exatn::createTensor("H",TENS_ELEM_TYPE,TensorShape{2,2}); assert(success);
+ success = exatn::initTensorData("H",exatn::quantum::getGateData(exatn::quantum::Gate::gate_H)); assert(success);
+ // Create and initialize 2-body gates:
+ success = exatn::createTensor("CNOT",TENS_ELEM_TYPE,TensorShape{2,2,2,2}); assert(success);
+ success = exatn::initTensorData("CNOT",exatn::quantum::getGateData(exatn::quantum::Gate::gate_CX)); assert(success);
+ // Append qubit tensors, 1-body and 2-body gates to a tensor network:
+ auto circuit_net = exatn::makeSharedTensorNetwork("Circuit");
+ for(unsigned int i = 0; i < num_sites; ++i){
+  success = circuit_net->appendTensor(i+1,exatn::getTensor("Q"+std::to_string(i)),{}); assert(success);
+ }
+ for(unsigned int i = 0; i < 1; ++i){
+  success = circuit_net->appendTensorGate(exatn::getTensor("H"),{i});
+ }
+ for(unsigned int i = 1; i < std::min(max_layers+1,num_sites); ++i){
+  success = circuit_net->appendTensorGate(exatn::getTensor("CNOT"),{i,i-1}); assert(success);
+ }
+ auto circuit = exatn::makeSharedTensorExpansion("Circuit",circuit_net,std::complex<double>{1.0,0.0});
+ success = exatn::balanceNormalizeNorm2Sync(*circuit,1.0,1.0,false); assert(success);
+ //circuit->printIt(); //debug
+
+ //Evaluate the quantum circuit:
+ if(EVALUATE_FULL_TENSOR){
+  success = exatn::evaluateSync(*circuit_net); assert(success);
+ }
+
+ //Create tensor network ansatz:
+ auto mps_builder = exatn::getTensorNetworkBuilder("MPS");
+ success = mps_builder->setParameter("max_bond_dim",max_bond_dim); assert(success);
+ auto ansatz_tensor = exatn::makeSharedTensor("AnsatzTensor",std::vector<int>(num_sites,2));
+ auto ansatz_net = exatn::makeSharedTensorNetwork("Ansatz",ansatz_tensor,*mps_builder);
+ ansatz_net->markOptimizableTensors([](const Tensor & tensor){return true;});
+ auto ansatz = exatn::makeSharedTensorExpansion("Ansatz",ansatz_net,std::complex<double>{1.0,0.0});
+ for(auto tens_conn = ansatz_net->begin(); tens_conn != ansatz_net->end(); ++tens_conn){
+  if(tens_conn->first != 0){ //input tensors only
+   success = exatn::createTensor(tens_conn->second.getTensor(),TENS_ELEM_TYPE); assert(success);
+   success = exatn::initTensorRnd(tens_conn->second.getName()); assert(success);
+  }
+ }
+ success = exatn::balanceNormalizeNorm2Sync(*ansatz,1.0,1.0,true); assert(success);
+ //ansatz->printIt(); //debug
+
+ //Create the full tensor ansatz:
+ auto ansatz_full_tensor = exatn::makeSharedTensor("AnsatzFullTensor",std::vector<int>(num_sites,2));
+ auto ansatz_full_net = exatn::makeSharedTensorNetwork("AnsatzFull");
+ success = ansatz_full_net->appendTensor(1,ansatz_full_tensor,{}); assert(success);
+ ansatz_full_net->markOptimizableAllTensors();
+ auto ansatz_full = exatn::makeSharedTensorExpansion("AnsatzFull",ansatz_full_net,std::complex<double>{1.0,0.0});
+ //ansatz_full->printIt(); //debug
+ if(EVALUATE_FULL_TENSOR){
+  success = exatn::createTensor(ansatz_full_tensor,TENS_ELEM_TYPE); assert(success);
+  success = exatn::initTensorRnd(ansatz_full_tensor->getName()); assert(success);
+  success = exatn::balanceNormalizeNorm2Sync(*ansatz_full,1.0,1.0,true); assert(success);
+ }
+
+ //Reconstruct the quantum circuit by a given tensor network ansatz:
+ std::cout << "Reconstructing the quantum circuit by a given tensor network ansatz:" << std::endl;
+ ansatz->conjugate();
+ exatn::TensorNetworkReconstructor::resetDebugLevel(1); //debug
+ exatn::TensorNetworkReconstructor reconstructor(circuit,ansatz,1e-5);
+ reconstructor.resetLearningRate(1.0);
+ success = exatn::sync(); assert(success);
+ double residual_norm, fidelity;
+ bool reconstructed = reconstructor.reconstruct(&residual_norm,&fidelity);
+ success = exatn::sync(); assert(success);
+ if(reconstructed){
+  std::cout << "Reconstruction succeeded: Residual norm = " << residual_norm
+            << "; Fidelity = " << fidelity << std::endl;
+  /*for(auto tens = ansatz_net->cbegin(); tens != ansatz_net->cend(); ++tens){
+   if(tens->first != 0) exatn::printTensor(tens->second.getName()); //debug
+  }*/
+  if(EVALUATE_FULL_TENSOR){
+   const auto & tensor0_name = ansatz_full_tensor->getName();
+   const auto & tensor1_name = circuit_net->getTensor(0)->getName();
+   success = exatn::initTensorSync(tensor0_name,0.0); assert(success);
+   success = exatn::evaluateSync(*ansatz,ansatz_full_tensor); assert(success);
+   success = exatn::normalizeNorm2Sync(tensor0_name); assert(success);
+   success = exatn::normalizeNorm2Sync(tensor1_name); assert(success);
+   std::string addition;
+   success = exatn::generate_addition_pattern(ansatz_full_tensor->getRank(),addition,false,tensor0_name,tensor1_name); assert(success);
+   success = exatn::addTensors(addition,-1.0); assert(success);
+   double norm2 = 0.0;
+   success = exatn::computeNorm2Sync(tensor0_name,norm2);
+   std::cout << "2-norm of the tensor-difference = " << norm2 << std::endl;
+   //std::cout << "Full circuit tensor (reference):" << std::endl; //debug
+   //exatn::printTensor(tensor1_name); //debug
+  }
+ }else{
+  std::cout << "Reconstruction failed!" << std::endl; //assert(false);
+ }
+ ansatz->conjugate();
+
+ //Destroy tensors:
+ if(EVALUATE_FULL_TENSOR){
+  success = exatn::destroyTensor(ansatz_full_tensor->getName()); assert(success);
+  success = exatn::destroyTensor(ansatz_tensor->getName()); assert(success);
+ }
+ success = exatn::destroyTensors(*ansatz_net); assert(success);
+ success = exatn::destroyTensors(*circuit_net); assert(success);
+
+ //Synchronize:
+ success = exatn::sync(); assert(success);
+ exatn::resetLoggingLevel(0,0);
+ //Grab a beer!
+}
+#endif
+
+#ifdef EXATN_TEST27
 TEST(NumServerTester, HubbardHamiltonian) {
  using exatn::TensorShape;
  using exatn::TensorSignature;
@@ -2968,8 +3111,8 @@ TEST(NumServerTester, HubbardHamiltonian) {
 }
 #endif
 
-#ifdef EXATN_TEST27
-TEST(NumServerTester, ExaTNGenVisitor) {
+#ifdef EXATN_TEST28
+TEST(NumServerTester, MCVQEHamiltonian) {
  using exatn::TensorShape;
  using exatn::TensorSignature;
  using exatn::Tensor;
@@ -2981,53 +3124,19 @@ TEST(NumServerTester, ExaTNGenVisitor) {
 
  const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX64;
 
- //Test configuration:
- const int num_sites = 16, max_bond_dim = std::min(static_cast<int>(std::pow(2,num_sites/2)),1);
- const int max_layers = (num_sites - 1); //1 less CNOT gates
- bool EVALUATE_FULL_TENSOR = false;
-
  //exatn::resetLoggingLevel(2,2); //debug
 
  bool success = true;
 
- //Define the initial qubit state vector:
- std::vector<std::complex<double>> qzero {
-  {1.0,0.0}, {0.0,0.0}
- };
+ const int num_sites = 8;
+ const int bond_dim_lim = 4;
+ const int max_bond_dim = std::min(static_cast<int>(std::pow(2,num_sites/2)),bond_dim_lim);
 
- //Create a shallow quantum circuit:
- // Create and initialize qubit tensors:
- for(unsigned int i = 0; i < num_sites; ++i){
-  success = exatn::createTensor("Q"+std::to_string(i),TENS_ELEM_TYPE,TensorShape{2}); assert(success);
- }
- for(unsigned int i = 0; i < num_sites; ++i){
-  success = exatn::initTensorData("Q"+std::to_string(i),qzero); assert(success);
- }
- // Create and initalize 1-body gates:
- success = exatn::createTensor("H",TENS_ELEM_TYPE,TensorShape{2,2}); assert(success);
- success = exatn::initTensorData("H",exatn::quantum::getGateData(exatn::quantum::Gate::gate_H)); assert(success);
- // Create and initialize 2-body gates:
- success = exatn::createTensor("CNOT",TENS_ELEM_TYPE,TensorShape{2,2,2,2}); assert(success);
- success = exatn::initTensorData("CNOT",exatn::quantum::getGateData(exatn::quantum::Gate::gate_CX)); assert(success);
- // Append qubit tensors, 1-body and 2-body gates to a tensor network:
- auto circuit_net = exatn::makeSharedTensorNetwork("Circuit");
- for(unsigned int i = 0; i < num_sites; ++i){
-  success = circuit_net->appendTensor(i+1,exatn::getTensor("Q"+std::to_string(i)),{}); assert(success);
- }
- for(unsigned int i = 0; i < 1; ++i){
-  success = circuit_net->appendTensorGate(exatn::getTensor("H"),{i});
- }
- for(unsigned int i = 1; i < std::min(max_layers+1,num_sites); ++i){
-  success = circuit_net->appendTensorGate(exatn::getTensor("CNOT"),{i,i-1}); assert(success);
- }
- auto circuit = exatn::makeSharedTensorExpansion("Circuit",circuit_net,std::complex<double>{1.0,0.0});
- success = exatn::balanceNormalizeNorm2Sync(*circuit,1.0,1.0,false); assert(success);
- //circuit->printIt(); //debug
-
- //Evaluate the quantum circuit:
- if(EVALUATE_FULL_TENSOR){
-  success = exatn::evaluateSync(*circuit_net); assert(success);
- }
+ //Read the Hamiltonian in spin representation:
+ auto hamiltonian_operator = exatn::quantum::readSpinHamiltonian("MCVQEHam",
+     "mcvqe_"+std::to_string(num_sites)+"q.qcw.txt",TENS_ELEM_TYPE,"QCWare");
+ success = hamiltonian_operator->deleteComponent(0); assert(success); //remove SCF part
+ hamiltonian_operator->printIt();
 
  //Create tensor network ansatz:
  auto mps_builder = exatn::getTensorNetworkBuilder("MPS");
@@ -3036,72 +3145,30 @@ TEST(NumServerTester, ExaTNGenVisitor) {
  auto ansatz_net = exatn::makeSharedTensorNetwork("Ansatz",ansatz_tensor,*mps_builder);
  ansatz_net->markOptimizableTensors([](const Tensor & tensor){return true;});
  auto ansatz = exatn::makeSharedTensorExpansion("Ansatz",ansatz_net,std::complex<double>{1.0,0.0});
- for(auto tens_conn = ansatz_net->begin(); tens_conn != ansatz_net->end(); ++tens_conn){
-  if(tens_conn->first != 0){ //input tensors only
-   success = exatn::createTensor(tens_conn->second.getTensor(),TENS_ELEM_TYPE); assert(success);
-   success = exatn::initTensorRnd(tens_conn->second.getName()); assert(success);
-  }
- }
- success = exatn::balanceNormalizeNorm2Sync(*ansatz,1.0,1.0,true); assert(success);
  //ansatz->printIt(); //debug
 
- //Create the full tensor ansatz:
- auto ansatz_full_tensor = exatn::makeSharedTensor("AnsatzFullTensor",std::vector<int>(num_sites,2));
- auto ansatz_full_net = exatn::makeSharedTensorNetwork("AnsatzFull");
- success = ansatz_full_net->appendTensor(1,ansatz_full_tensor,{}); assert(success);
- ansatz_full_net->markOptimizableAllTensors();
- auto ansatz_full = exatn::makeSharedTensorExpansion("AnsatzFull",ansatz_full_net,std::complex<double>{1.0,0.0});
- //ansatz_full->printIt(); //debug
- if(EVALUATE_FULL_TENSOR){
-  success = exatn::createTensor(ansatz_full_tensor,TENS_ELEM_TYPE); assert(success);
-  success = exatn::initTensorRnd(ansatz_full_tensor->getName()); assert(success);
-  success = exatn::balanceNormalizeNorm2Sync(*ansatz_full,1.0,1.0,true); assert(success);
- }
+ //Allocate/initialize tensors in the tensor network ansatz:
+ success = exatn::createTensorsSync(*ansatz_net,TENS_ELEM_TYPE); assert(success);
+ success = exatn::initTensorsRndSync(*ansatz_net); assert(success);
+ //success = exatn::balanceNormalizeNorm2Sync(*ansatz,1.0,1.0,true); assert(success);
 
- //Reconstruct the quantum circuit by a given tensor network ansatz:
- std::cout << "Reconstructing the quantum circuit by a given tensor network ansatz:" << std::endl;
- ansatz->conjugate();
- exatn::TensorNetworkReconstructor::resetDebugLevel(1); //debug
- exatn::TensorNetworkReconstructor reconstructor(circuit,ansatz,1e-5);
- reconstructor.resetLearningRate(1.0);
- success = exatn::sync(); assert(success);
- double residual_norm, fidelity;
- bool reconstructed = reconstructor.reconstruct(&residual_norm,&fidelity);
- success = exatn::sync(); assert(success);
- if(reconstructed){
-  std::cout << "Reconstruction succeeded: Residual norm = " << residual_norm
-            << "; Fidelity = " << fidelity << std::endl;
-  /*for(auto tens = ansatz_net->cbegin(); tens != ansatz_net->cend(); ++tens){
-   if(tens->first != 0) exatn::printTensor(tens->second.getName()); //debug
-  }*/
-  if(EVALUATE_FULL_TENSOR){
-   const auto & tensor0_name = ansatz_full_tensor->getName();
-   const auto & tensor1_name = circuit_net->getTensor(0)->getName();
-   success = exatn::initTensorSync(tensor0_name,0.0); assert(success);
-   success = exatn::evaluateSync(*ansatz,ansatz_full_tensor); assert(success);
-   success = exatn::normalizeNorm2Sync(tensor0_name); assert(success);
-   success = exatn::normalizeNorm2Sync(tensor1_name); assert(success);
-   std::string addition;
-   success = exatn::generate_addition_pattern(ansatz_full_tensor->getRank(),addition,false,tensor0_name,tensor1_name); assert(success);
-   success = exatn::addTensors(addition,-1.0); assert(success);
-   double norm2 = 0.0;
-   success = exatn::computeNorm2Sync(tensor0_name,norm2);
-   std::cout << "2-norm of the tensor-difference = " << norm2 << std::endl;
-   //std::cout << "Full circuit tensor (reference):" << std::endl; //debug
-   //exatn::printTensor(tensor1_name); //debug
+ //Perform ground state optimization on a tensor network manifold:
+ {
+  std::cout << "Ground state optimization on a tensor network manifold:" << std::endl;
+  exatn::TensorNetworkOptimizer::resetDebugLevel(1);
+  exatn::TensorNetworkOptimizer optimizer(hamiltonian_operator,ansatz,5e-4);
+  //optimizer.resetMicroIterations(1);
+  bool converged = optimizer.optimize();
+  success = exatn::sync(); assert(success);
+  if(converged){
+   std::cout << "Optimization succeeded!" << std::endl;
+  }else{
+   std::cout << "Optimization failed!" << std::endl; assert(false);
   }
- }else{
-  std::cout << "Reconstruction failed!" << std::endl; //assert(false);
  }
- ansatz->conjugate();
 
  //Destroy tensors:
- if(EVALUATE_FULL_TENSOR){
-  success = exatn::destroyTensor(ansatz_full_tensor->getName()); assert(success);
-  success = exatn::destroyTensor(ansatz_tensor->getName()); assert(success);
- }
- success = exatn::destroyTensors(*ansatz_net); assert(success);
- success = exatn::destroyTensors(*circuit_net); assert(success);
+ success = exatn::destroyTensorsSync(*ansatz_net); assert(success);
 
  //Synchronize:
  success = exatn::sync(); assert(success);
@@ -3110,7 +3177,7 @@ TEST(NumServerTester, ExaTNGenVisitor) {
 }
 #endif
 
-#ifdef EXATN_TEST28
+#ifdef EXATN_TEST30
 TEST(NumServerTester, TensorComposite) {
  using exatn::TensorShape;
  using exatn::TensorSignature;

src/exatn/tests/mcvqe_16q.qcw.txt

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src/exatn/tests/mcvqe_24q.qcw.txt

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