Commit 50ec48a0 authored by Nguyen, Thien Minh's avatar Nguyen, Thien Minh
Browse files

Added autograd with args_translator 



Signed-off-by: default avatarThien Nguyen <nguyentm@ornl.gov>
parent 0e373195

examples/CMakeLists.txt

+1 −0
Original line number Diff line number Diff line
@@ -40,6 +40,7 @@ add_test(NAME hadamard_ctrl_test COMMAND ${CMAKE_BINARY_DIR}/qcor ${CMAKE_CURREN 
add_test(NAME multi_ctrl_test COMMAND ${CMAKE_BINARY_DIR}/qcor ${CMAKE_CURRENT_SOURCE_DIR}/ctrl-gates/multiple_controls.cpp)



add_qcor_compile_and_exe_test(qrt_obj_func_simple simple/simple-objective-function.cpp)

add_qcor_compile_and_exe_test(qrt_kernel_autograd_simple simple/gradients_optimization.cpp)

add_qcor_compile_and_exe_test(qrt_bell_ctrl bell/bell_control.cpp)



# Lambda tests

examples/simple/gradients_optimization.cpp

+30 −0
Original line number Diff line number Diff line
@@ -11,6 +11,13 @@ __qpu__ void ansatz_vec(qreg q, std::vector<double> angles) { 
  CX(q[1], q[0]);

}



// Ansatz with an arbitrary signature

__qpu__ void ansatz_complex(qreg q, int idx, std::vector<double> angles) {

  X(q[0]);

  Ry(q[1], angles[idx]);

  CX(q[1], q[0]);

}



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

  // Allocate 2 qubits

  auto q = qalloc(2);
@@ -64,4 +71,27 @@ int main(int argc, char **argv) { 
    print(energy);

    qcor_expect(std::abs(energy + 1.74886) < 0.1);

  }

  {

    // Create the Optimizer (gradient-based)

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

    OptFunction opt_function(

        [&](const std::vector<double> &x, std::vector<double> &dx) {

          // Using kernel auto-gradient helper

          // Needs to provide args translator for this complex kernel.

          auto quantum_reg = qalloc(2);

          int index = 0;

          ArgsTranslator<qreg, int, std::vector<double>> args_translation(

              [&](const std::vector<double> x) {

                return std::tuple(quantum_reg, index, x);

              });

          auto exp = ansatz_complex::autograd(H, dx, x, args_translation);

          print("<E(", x[0], ") = ", exp);

          return exp;

        },

        n_variational_params);



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

    print(energy);

    qcor_expect(std::abs(energy + 1.74886) < 0.1);

  }

}

runtime/kernel/quantum_kernel.hpp

+31 −0
Original line number Diff line number Diff line
@@ -231,6 +231,37 @@ class QuantumKernel { 
    return cost_val;

  }



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

                         std::vector<double> x,

                         ArgsTranslator<Args...> args_translator) {

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

        std::vector<double>)>

        kernel_eval = [&](std::vector<double> x_vec) {

          auto eval_lambda = [&](Args... args) {

            auto tempKernel =

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

            Derived derived(args...);

            derived.disable_destructor = true;

            derived(args...);

            tempKernel->addInstructions(

                derived.parent_kernel->getInstructions());

            return tempKernel;

          };

          auto args_tuple = args_translator(x_vec);

          return std::apply(eval_lambda, args_tuple);

        };



    auto gradiend_method = qcor::__internal__::get_gradient_method(

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



    auto kernel_observe = [&](Args... args) { return observe(obs, args...); };



    auto args_tuple = args_translator(x);

    const double cost_val = std::apply(kernel_observe, args_tuple);

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

    return cost_val;

  }



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

    Derived derived(args...);

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