Merge branch 'master' into tnguyen/opt-data-upstream (3cbf83cd) · Commits · ORNL Quantum Computing Institute / qcor

examples/CMakeLists.txt

+1 −1

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		@@ -10,4 +10,4 @@ add_test(NAME qrt_qpe_example COMMAND ${CMAKE_BINARY_DIR}/qcor -c -I${CMAKE_CURR
		add_test(NAME qrt_kernel_include COMMAND ${CMAKE_BINARY_DIR}/qcor -c -I${CMAKE_CURRENT_SOURCE_DIR}/shared ${CMAKE_CURRENT_SOURCE_DIR}/simple/multiple_kernels.cpp)
		add_test(NAME qrt_period_finding COMMAND ${CMAKE_BINARY_DIR}/qcor -c -I${CMAKE_CURRENT_SOURCE_DIR}/shared ${CMAKE_CURRENT_SOURCE_DIR}/simple/period_finding.cpp)
		add_test(NAME qrt_grover COMMAND ${CMAKE_BINARY_DIR}/qcor -c ${CMAKE_CURRENT_SOURCE_DIR}/grover/grover.cpp)

		add_test(NAME qrt_adapt COMMAND ${CMAKE_BINARY_DIR}/qcor -c ${CMAKE_CURRENT_SOURCE_DIR}/hybrid/adapt_h2.cpp)
		No newline at end of file

+9 −23

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		#include "qcor.hpp"

		__qpu__ void ansatz(qreg q, double theta, std::shared_ptr<xacc::Observable> H) {
		__qpu__ void ansatz(qreg q, double theta) {
		X(q[0]);
		exp_i_theta(q, theta, H);
		auto exponent_op = X(0) * Y(1) - Y(0) * X(1);
		exp_i_theta(q, theta, exponent_op);
		}

		int main(int argc, char **argv) {
		@@ -10,31 +10,17 @@ int main(int argc, char **argv) {
		auto q = qalloc(2);

		// Create the Deuteron Hamiltonian
		auto H = qcor::createObservable(
		auto H = createObservable(
		"5.907 - 2.1433 X0X1 - 2.1433 Y0Y1 + .21829 Z0 - 6.125 Z1");

		// Create the Ansatz exponent Operator
		auto ansatz_exponent = qcor::createObservable("X0 Y1 - Y0 X1");

		// Create an objective function to optimize
		// Each call evaluates E(theta) = <ansatz(theta) \| H \| ansatz(theta)>
		qcor::OptFunction opt_func(
		[&](const std::vector<double> &x, std::vector<double> &grad) -> double {

		// Affect Observable observation and evaluate at given ansatz parameters
		auto e = qcor::observe(ansatz, H, q, x[0], ansatz_exponent);

		// Need to clean the current qubit register
		q.reset();
		return e;
		},
		1);
		// Create the objective function
		auto objective = createObjectiveFunction(ansatz, H, q, 1);

		// Create a qcor Optimizer
		auto optimizer = qcor::createOptimizer("nlopt");
		auto optimizer = createOptimizer("nlopt");

		// Optimize the above function
		auto result = optimizer->optimize(opt_func);
		auto result = optimizer->optimize(*objective.get());

		// Print the result
		printf("energy = %f\n", result.first);

+8 −16

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		#include "qcor.hpp"

		__qpu__ void ansatz(qreg q, double theta) {
		X(q[0]);
		@@ -10,31 +9,24 @@ int main(int argc, char **argv) {
		// Allocate 2 qubits
		auto q = qalloc(2);

		// Create the Deuteron Hamiltonian (Observable)
		auto H = 5.907 - 2.1433 * qcor::X(0) * qcor::X(1) -
		2.1433 * qcor::Y(0) * qcor::Y(1) + .21829 * qcor::Z(0) -
		6.125 * qcor::Z(1);
		// Create the Deuteron Hamiltonian
		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 and the Observable
		auto objective = qcor::createObjectiveFunction("vqe", ansatz, H);
		// Create the ObjectiveFunction
		auto objective = qcor::createObjectiveFunction(ansatz, H, q, 1);

		// Create the Optimizer
		auto optimizer = qcor::createOptimizer("nlopt");
		auto optimizer = createOptimizer("nlopt");

		// Call taskInitiate, kick off optimization of the give
		// functor dependent on the ObjectiveFunction, async call
		auto handle = qcor::taskInitiate(
		objective, optimizer,
		[&](const std::vector<double> x, std::vector<double> &dx) {
		return (*objective)(q, x[0]);
		},
		1);
		auto handle = taskInitiate(objective, optimizer);

		// Go do other work...

		// Query results when ready.
		auto results = qcor::sync(handle);
		auto results = sync(handle);

		// Print the optimal value.
		printf("<H> = %f\n", results.opt_val);

0 → 100644

+39 −0

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		#include "qcor_hybrid.hpp"
		#include "qcor_utils.hpp"
		#include <iomanip>

		// Define the state preparation kernel
		__qpu__ void initial_state(qreg q) {
		X(q[0]);
		X(q[2]);
		}

		int main() {

		// Define the Hamiltonian using the QCOR API
		auto H = 0.1202 * Z(0) * Z(1) + 0.168336 * Z(0) * Z(2) +
		0.1202 * Z(2) * Z(3) + 0.17028 * Z(2) + 0.17028 * Z(0) +
		0.165607 * Z(0) * Z(3) + 0.0454063 * Y(0) * Y(1) * X(2) * X(3) -
		0.106477 - 0.220041 * Z(3) + 0.174073 * Z(1) * Z(3) +
		0.0454063 * Y(0) * Y(1) * Y(2) * Y(3) - 0.220041 * Z(1) +
		0.165607 * Z(1) * Z(2) + 0.0454063 * X(0) * X(1) * Y(2) * Y(3) +
		0.0454063 * X(0) * X(1) * X(2) * X(3);

		// optimizer
		auto optimizer = qcor::createOptimizer(
		"nlopt", {std::make_pair("nlopt-optimizer", "l-bfgs")});

		// Create ADAPT-VQE instance
		// Run H2 with the singlet-adapted-uccsd pool
		ADAPT adapt(initial_state, H, optimizer,
		{{"sub-algorithm", "vqe"},
		{"pool", "singlet-adapted-uccsd"},
		{"n-electrons", 2},
		{"gradient_strategy", "central"}});

		// Execute!
		auto energy = adapt.execute();

		// Print the energy
		std::cout << std::setprecision(12) << energy << "\n";
		}

+0 −5

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		@@ -27,11 +27,6 @@ int main() {
		// Create the QAOA instance
		QAOA qaoa(H, ref_ham, steps);

		// we're using a gradient-based optimizer,
		// by default QAOA will use parameter-shift-rule,
		// here we set the scale factor.
		qaoa.set_parameter_shift_scale_factor(.1);

		// Execute synchronously and display
		const auto [energy, params] = qaoa.execute(lbfgs);