Commit 8a20c22e authored by Nguyen, Thien Minh's avatar Nguyen, Thien Minh
Browse files

Added autograd helper for double and vector 



also, fixed a copy-and-paste error

Signed-off-by: default avatarThien Nguyen <nguyentm@ornl.gov>
parent af2778fe

examples/simple/gradients_optimization.cpp

+1 −6
Original line number Diff line number Diff line
@@ -16,11 +16,6 @@ int main(int argc, char **argv) { 
  auto H = 5.907 - 2.1433 * X(0) * X(1) - 2.1433 * Y(0) * Y(1) + .21829 * Z(0) -

           6.125 * Z(1);



  // Create the ObjectiveFunction, here we want to run VQE

  // need to provide ansatz, Operator, and qreg

  auto objective = createObjectiveFunction(ansatz, H, q, n_variational_params,

                                           {{"gradient-strategy", "central"}});



  // Create the Optimizer.

  auto optimizer = createOptimizer("nlopt", {{"nlopt-optimizer", "l-bfgs"}});

  OptFunction opt_function(
@@ -30,7 +25,7 @@ int main(int argc, char **argv) { 
        print("<E(", x[0], ") = ", exp);

        return exp;

      },

      1);

      n_variational_params);



  auto [energy, opt_params] = optimizer->optimize(opt_function);

  print(energy);

runtime/kernel/quantum_kernel.hpp

+27 −1
Original line number Diff line number Diff line
@@ -194,8 +194,13 @@ class QuantumKernel { 
    std::function<std::shared_ptr<xacc::CompositeInstruction>(

        std::vector<double>)>

        kernel_eval = [q](std::vector<double> x) {

          auto tempKernel =

              qcor::__internal__::create_composite("__temp__autograd__");

          Derived derived(q, x[0]);

          return derived.parent_kernel;

          derived.disable_destructor = true;

          derived(q, x[0]);

          tempKernel->addInstructions(derived.parent_kernel->getInstructions());

          return tempKernel;

        };



    auto gradiend_method = qcor::__internal__::get_gradient_method(
@@ -205,6 +210,27 @@ class QuantumKernel { 
    return cost_val;

  }



  static double autograd(Observable &obs, std::vector<double> &dx, qreg q,

                         std::vector<double> x) {

    std::function<std::shared_ptr<xacc::CompositeInstruction>(

        std::vector<double>)>

        kernel_eval = [q](std::vector<double> x) {

          auto tempKernel =

              qcor::__internal__::create_composite("__temp__autograd__");

          Derived derived(q, x);

          derived.disable_destructor = true;

          derived(q, x);

          tempKernel->addInstructions(derived.parent_kernel->getInstructions());

          return tempKernel;

        };



    auto gradiend_method = qcor::__internal__::get_gradient_method(

        qcor::__internal__::DEFAULT_GRADIENT_METHOD, kernel_eval, obs);

    const double cost_val = observe(obs, q, x);

    dx = (*gradiend_method)(x, cost_val);

    return cost_val;

  }



  static std::string openqasm(Args... args) {

    Derived derived(args...);

    KernelSignature<Args...> callable(derived);

runtime/objectives/gradients/FiniteDifference/FiniteDifferenceGradients.cpp

+1 −1
Original line number Diff line number Diff line
@@ -144,7 +144,7 @@ public: 
    m_obs = xacc::as_shared_ptr(&obs);

    gradient_func = [&](const std::vector<double> &x,

                        double cost_val) -> std::vector<double> {

      return run_gradient_strategy(x, cost_val, "forward", m_step, m_obs,

      return run_gradient_strategy(x, cost_val, "central", m_step, m_obs,

                                   m_kernel_eval);

    };

  }