def run_vqe(H, ansatz, params=None):
from pennylane.optimize import NesterovMomentumOptimizer, AdamOptimizer
num_qubits = len(H.wires)
num_layers = 4
if params is None:
params = qml.init.strong_ent_layers_uniform(num_layers, num_qubits, 3)
cost_fn = qml.ExpvalCost(ansatz, H, dev)
stepsize = 0.1
opt = NesterovMomentumOptimizer(stepsize)
max_iterations = 300
conv_tol = 1e-8
energy = 0
for n in range(max_iterations):
params, prev_energy = opt.step_and_cost(cost_fn, params)
energy = cost_fn(params)
conv = np.abs(energy - prev_energy)
stepsize *= 0.99
opt.update_stepsize(stepsize)
if DEBUG and n % 20 == 0:
print('Iteration = {:}, Energy = {:.8f} Ha'.format(n, energy))
if conv <= conv_tol:
break
return energy, params
def run_vqe_excited2(H, ansatz, gs_params, fes_params, params=None):
from pennylane.optimize import NesterovMomentumOptimizer, AdamOptimizer
num_qubits = len(H.wires)
num_layers = 4
if params is None:
params = qml.init.strong_ent_layers_uniform(num_layers, num_qubits, 3)
@qml.qnode(dev)
def overlap(params, wires):
variational_ansatz(gs_params, wires)
qml.inv(qml.template(variational_ansatz)(params, wires))
return qml.probs([0, 1, 2])
@qml.qnode(dev)
def overlap2(params, wires):
variational_ansatz(fes_params, wires)
qml.inv(qml.template(variational_ansatz)(params, wires))
return qml.probs([0, 1, 2])
def cost_fn(params, **kwargs):
h_cost = qml.ExpvalCost(ansatz, H, dev)
h = h_cost(params, **kwargs)
o = overlap(params, wires=H.wires)
o2 = overlap2(params, wires=H.wires)
return h + 1.5 * o[0] + o2[0]
stepsize = 0.3
opt = NesterovMomentumOptimizer(stepsize)
max_iterations = 300
conv_tol = 1e-8
energy = 0
for n in range(max_iterations):
params, prev_energy = opt.step_and_cost(cost_fn, params)
energy = cost_fn(params)
conv = np.abs(energy - prev_energy)
stepsize *= 0.99
opt.update_stepsize(stepsize)
if DEBUG and n % 20 == 0:
print('Iteration = {:}, Energy = {:.8f} Ha'.format(n, energy))
if conv <= conv_tol:
break
return energy, params
def test_step_and_cost_autograd_nesterov_multid_array(self):
"""Test that the correct cost is returned via the step_and_cost method for the
Nesterov momentum optimizer"""
stepsize, gamma = 0.1, 0.5
nesmom_opt = NesterovMomentumOptimizer(stepsize, momentum=gamma)
multid_array = np.array([[0.1, 0.2], [-0.1, -0.4]])
@qml.qnode(qml.device("default.qubit", wires=1))
def quant_fun_mdarr(var):
qml.RX(var[0, 1], wires=[0])
qml.RY(var[1, 0], wires=[0])
qml.RY(var[1, 1], wires=[0])
return qml.expval(qml.PauliZ(0))
_, res = nesmom_opt.step_and_cost(quant_fun_mdarr, multid_array)
expected = quant_fun_mdarr(multid_array)
assert np.all(res == expected)
def test_step_and_cost_autograd_nesterov_multiple_inputs(self):
"""Test that the correct cost is returned via the step_and_cost method for the
Nesterov momentum optimizer"""
stepsize, gamma = 0.1, 0.5
nesmom_opt = NesterovMomentumOptimizer(stepsize, momentum=gamma)
@qml.qnode(qml.device("default.qubit", wires=1))
def quant_fun(*variables):
qml.RX(variables[0][1], wires=[0])
qml.RY(variables[1][2], wires=[0])
qml.RY(variables[2], wires=[0])
return qml.expval(qml.PauliZ(0))
inputs = [
np.array((0.2, 0.3), requires_grad=True),
np.array([0.4, 0.2, 0.4], requires_grad=True),
np.array(0.1, requires_grad=True),
]
_, res = nesmom_opt.step_and_cost(quant_fun, *inputs)
expected = quant_fun(*inputs)
assert np.all(res == expected)