def test_trial_functions(self):
"""trial_circuit_ry function"""
entangler_map = {0: [2], 1: [2], 3: [2], 4: [2]}
m = 1
n = 6
theta = np.zeros(m * n)
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 456)
self.log.info("With No measurement:\n")
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, None,
None)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 324)
self.log.info("With Y measurement:\n")
meas_sting = ['Y' for x in range(n)]
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map,
meas_sting)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 564)
def obj_funct(Q_program, pauli_list, entangler_map, coupling_map, initial_layout, n, m, backend, shots, theta):
""" Evaluate the objective function for a classical optimization problem.
Q_program is an instance object of the class quantum program
pauli_list defines the cost function as list of ising terms with weights
theta are the control parameters
n is the number of qubits
m is the depth of the trial function
backend is the type of backend to run it on
shots is the number of shots to run. Taking shots = 1 only works in simulation
and computes an exact average of the cost function on the quantum state
"""
std_cost=0 # to add later
circuits = ['trial_circuit']
if shots==1:
Q_program.add_circuit('trial_circuit', trial_circuit_ry(n, m, theta, entangler_map, None, False))
result = Q_program.execute(circuits, backend=backend, coupling_map=coupling_map, initial_layout=initial_layout, shots=shots)
state = result.get_data('trial_circuit')['quantum_state']
cost=Energy_Estimate_Exact(state,pauli_list,True)
else:
Q_program.add_circuit('trial_circuit', trial_circuit_ry(n, m, theta, entangler_map, None, True))
result = Q_program.execute(circuits, backend=backend, coupling_map=coupling_map, initial_layout=initial_layout, shots=shots)
data = result.get_counts('trial_circuit')
cost = Energy_Estimate(data, pauli_list)
return cost, std_cost
def test_trial_functions(self):
"""trial_circuit_ry function"""
entangler_map = {0: [2], 1: [2], 3: [2], 4: [2]}
m = 1
n = 6
theta = np.zeros(m * n)
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 456)
self.log.info("With No measurement:\n")
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, None, None)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 324)
self.log.info("With Y measurement:\n")
meas_sting = ['Y' for x in range(n)]
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, meas_sting)
qasm_txt = trial_circuit.qasm()
self.log.info(qasm_txt)
self.assertEqual(len(qasm_txt), 564)
def test_eval_hamiltonian_pauli_list(self):
"""
Test of trial_circuit_ry and eval_hamiltonian with a pauli list
"""
ham_name = self._get_resource_path(
"../performance/H2/H2Equilibrium.txt")
pauli_list = Hamiltonian_from_file(ham_name)
n = 2
m = 6
device = 'local_statevector_simulator'
theta = np.array([
-0.607547697211, -0.126136414606, -0.684606358705, 0.928714748593,
-1.84440103405, -0.467002424077, 2.29249034315, 0.488810054396,
0.710266990661, 1.05553444322, 0.0540731003458, 0.257953416342,
0.588281649703, 0.885244238613, -1.01700702421, -0.133693031283,
-0.438185501332, 0.493443494428, -0.199009119848, -1.27498360732,
0.293494154438, 0.108950311827, 0.031726785859, 1.27263986303
])
entangler_map = {0: [1]}
energy = eval_hamiltonian(
QuantumProgram(), pauli_list,
trial_circuit_ry(n, m, theta, entangler_map, None, False), 1,
device)
np.testing.assert_almost_equal(
-0.45295043823057191 + 3.3552033732997923e-18j, energy)
def test_trial_functions(self):
entangler_map = {0: [2], 1: [2], 3: [2], 4: [2]}
m = 1
n = 6
theta = np.zeros(m * n)
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map)
self.log.info(trial_circuit.qasm())
self.log.info("With No measurement:\n")
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, None, None)
self.log.info(trial_circuit.qasm())
self.log.info("With Y measurement:\n")
meas_sting = ['Y' for x in range(n)]
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, meas_sting)
self.log.info(trial_circuit.qasm())
def test_trial_functions(self):
entangler_map = {0: [2], 1: [2], 3: [2], 4: [2]}
m = 1
n = 6
theta = np.zeros(m * n)
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map)
self.log.info(trial_circuit.qasm())
self.log.info("With No measurement:\n")
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, None, None)
self.log.info(trial_circuit.qasm())
self.log.info("With Y measurement:\n")
meas_sting = ['Y' for x in range(n)]
trial_circuit = trial_circuit_ry(n, m, theta, entangler_map, meas_sting)
self.log.info(trial_circuit.qasm())
def test_eval_hamiltonian_pauli_list(self):
"""
Test of trial_circuit_ry and eval_hamiltonian with a pauli list
"""
ham_name = self._get_resource_path("../performance/H2/H2Equilibrium.txt")
pauli_list = Hamiltonian_from_file(ham_name)
n = 2
m = 6
device = 'local_statevector_simulator'
theta = np.array([-0.607547697211, -0.126136414606, - 0.684606358705, 0.928714748593,
-1.84440103405, -0.467002424077, 2.29249034315, 0.488810054396,
0.710266990661, 1.05553444322, 0.0540731003458, 0.257953416342,
0.588281649703, 0.885244238613, -1.01700702421, -0.133693031283,
-0.438185501332, 0.493443494428, -0.199009119848, -1.27498360732,
0.293494154438, 0.108950311827, 0.031726785859, 1.27263986303])
entangler_map = {0: [1]}
energy = eval_hamiltonian(QuantumProgram(), pauli_list,
trial_circuit_ry(n, m, theta, entangler_map, None, False), 1,
device)
np.testing.assert_almost_equal(-0.45295043823057191 + 3.3552033732997923e-18j, energy)
def cost_function(Q_program,H,n,m,entangler_map,shots,device,theta):
return eval_hamiltonian(Q_program,H,trial_circuit_ry(n,m,theta,entangler_map,None,False),shots,device).real
initial_theta,SPSA_params,max_trials,save_step,1);
plt.plot(np.arange(0, max_trials,save_step), cost_plus,label='C(theta_plus)')
plt.plot(np.arange(0, max_trials,save_step),cost_minus,label='C(theta_minus)')
plt.plot(np.arange(0, max_trials,save_step),np.ones(max_trials//save_step)*best_distance_quantum, label='Final Optimized Cost')
plt.plot(np.arange(0, max_trials,save_step),np.ones(max_trials//save_step)*exact, label='Exact Cost')
plt.legend()
plt.xlabel('Number of trials')
plt.ylabel('Cost')
shots = 5000
circuits = ['final_circuit']
Q_program.add_circuit('final_circuit', trial_circuit_ry(n, m, best_theta, entangler_map, None, True))
result = Q_program.execute(circuits, backend=backend, shots=shots, coupling_map=coupling_map, initial_layout=initial_layout)
data = result.get_counts('final_circuit')
plot_histogram(data,5)
# Getting the solution and cost from the largest component of the optimal quantum state
max_value = max(data.values()) # maximum value
max_keys = [k for k, v in data.items() if v == max_value] # getting all keys containing the `maximum`
x_quantum=np.zeros(n)
for bit in range(n):
if max_keys[0][bit]=='1':
label='C(theta_minus)')
plt.plot(np.arange(0, max_trials, save_step),
np.ones(max_trials // save_step) * best_distance_quantum,
label='Final Optimized Cost')
plt.plot(np.arange(0, max_trials, save_step),
np.ones(max_trials // save_step) * exact,
label='Exact Cost')
plt.legend()
plt.xlabel('Number of trials')
plt.ylabel('Cost')
shots = 5000
circuits = ['final_circuit']
Q_program.add_circuit(
'final_circuit',
trial_circuit_ry(n, m, best_theta, entangler_map, None, True))
result = Q_program.execute(circuits,
backend=backend,
shots=shots,
coupling_map=coupling_map,
initial_layout=initial_layout)
data = result.get_counts('final_circuit')
plot_histogram(data, 5)
# Getting the solution and cost from the largest component of the optimal quantum state
max_value = max(data.values()) # maximum value
max_keys = [k for k, v in data.items()
if v == max_value] # getting all keys containing the `maximum`
x_quantum = np.zeros(n)