Commit 93ccfedd authored by Nguyen, Thien Minh's avatar Nguyen, Thien Minh
Browse files

Added cpp and python examples for qsim qaoa and qite workflows 



Added an overload for PauliOp-only model.

Signed-off-by: default avatarThien Nguyen <nguyentm@ornl.gov>
parent 3b5d482b

examples/qsim/QaoaWorkflow.cpp

0 → 100644
+26 −0
Original line number Diff line number Diff line 
#include "qcor_qsim.hpp"



// Demonstrate qsim's QAOA workflow 



// Compile and run with:

/// $ qcor -qpu qpp QaoaWorkflow.cpp

/// $ ./a.out



int main(int argc, char **argv) {

  // Create the Deuteron Hamiltonian

  auto H = 5.907 - 2.1433 * X(0) * X(1) - 2.143 * Y(0) * Y(1) + 0.21829 * Z(0) -

           6.125 * Z(1);

  auto problemModel = qsim::ModelBuilder::createModel(H);

  auto optimizer = createOptimizer("nlopt");

  // Instantiate a QAOA workflow with the nlopt optimizer

  // "steps" = the (p) param in QAOA algorithm.

  auto workflow = qsim::getWorkflow("qaoa", {{"optimizer", optimizer}, {"steps", 8}});



  // Result should contain the ground-state energy along with the optimal

  // parameters.

  auto result = workflow->execute(problemModel);



  const auto energy = result.get<double>("energy");

  std::cout << "Ground-state energy = " << energy << "\n";

  return 0;

}
 
 No newline at end of file

examples/qsim/QiteWorkflow.cpp

0 → 100644
+27 −0
Original line number Diff line number Diff line 
#include "qcor_qsim.hpp"



// Demonstrate qsim's QITE workflow

// with a simple 1-qubit Hamiltonian from

// https://www.nature.com/articles/s41567-019-0704-4 Compile and run with:

/// $ qcor -qpu qpp QiteWorkflow.cpp

/// $ ./a.out



int main(int argc, char **argv) {

  auto ham = 0.7071067811865475 * X(0) + 0.7071067811865475 * Z(0);

  // Number of QITE time steps and step size

  const int nbSteps = 25;

  const double stepSize = 0.1;

  auto problemModel = qsim::ModelBuilder::createModel(ham);

  auto workflow =

      qsim::getWorkflow("qite", {{"steps", nbSteps}, {"step-size", stepSize}});

  auto result = workflow->execute(problemModel);

  const auto energy = result.get<double>("energy");

  const auto energyAtStep = result.get<std::vector<double>>("exp-vals");

  std::cout << "QITE energy: [ ";

  for (const auto &val : energyAtStep) {

    std::cout << val << " ";

  }

  std::cout << "]\n";

  std::cout << "Ground state energy: " << energy << "\n";

  return 0;

}
 
 No newline at end of file

lib/qsim/base/qcor_qsim.hpp

+5 −0
Original line number Diff line number Diff line
@@ -91,6 +91,11 @@ public: 
  // the observable of interest.

  static QuantumSimulationModel createModel(Observable *obs,

                                           const HeterogeneousMap &params = {});

  static QuantumSimulationModel

  createModel(PauliOperator &obs, const HeterogeneousMap &params = {}) {

    return createModel(&obs, params);

  }

  

  // Build a time-dependent problem model:

  //  -  obs: observable operator to measure.

  //  -  td_ham: time-dependent Hamiltonian to evolve the system.

python/examples/README.md

+4 −0
Original line number Diff line number Diff line
@@ -14,6 +14,10 @@ Employing the QCOR just-in-time (qjit) compilation features, we can wrap QCOR in 


`qsim_vqe.py` qsim example of VQE routine for Deuteron Hamiltonian.



`qsim_deuteron_qaoa.py` qsim example of QAOA routine for Deuteron Hamiltonian.



`qsim_qite_simple.py` qsim example of QITE (Quantum Imaginary Time Evolution) routine for a simple Hamiltonian.



`vqe_qcor_spec.py` example of VQE using `taskInitiate` for asynchronous execution of quantum-classical hybrid computations. 



`pyscf_qubit_tapering.py` example of using the QCOR `OperatorTransform`, specifically running [Qubit Tapering](https://arxiv.org/abs/1701.08213) followed by VQE using the QSim library.

python/examples/qsim_deuteron_qaoa.py

0 → 100644
+20 −0
Original line number Diff line number Diff line 
from qcor import * 



# Solving deuteron problem using qsim QAOA



# Define the deuteron hamiltonian 

H = -2.1433 * X(0) * X(1) - 2.1433 * \

    Y(0) * Y(1) + .21829 * Z(0) - 6.125 * Z(1) + 5.907



problemModel = qsim.ModelBuilder.createModel(H)

      

# Create the NLOpt derivative free optimizer

optimizer = createOptimizer('nlopt')



# Create the QAOA workflow with p = 8 steps

workflow = qsim.getWorkflow('qaoa', {'optimizer': optimizer, 'steps': 8})



# Execute and print the result

result = workflow.execute(problemModel)

energy = result['energy']

print(energy)
 
 No newline at end of file