def simulate(size, ansatz_name, minimizer, repeats, hamiltonian_idx, max_meas,
samples, h, eig):
H, qc, ansatz_, initial_params = \
core.interface.create_and_convert(ansatz_name, h)
if minimizer == 'nelder-mead':
vqe = vqe_nelder_mead(samples=samples, H=H, fatol=0, xatol=0)
tol_para = 1e-3
max_same_para = 3
callback = cb.restart(max_same_para, tol_para)
elif minimizer == 'bayes':
n_calls = int(round(max_meas/samples))
vqe = vqe_bayes(n_calls=n_calls)
def callback(*args, **kwargs):
pass
if ansatz_name == 'multi_particle':
initial_params = [(-1.0, 1.0)]*(size - 1)
elif ansatz_name == 'one_particle_ucc':
initial_params = [(-3.0, 3.0)] * (size - 1)
else:
raise RuntimeError("Don't know that ansatz.")
else:
raise RuntimeError('Bad minimizer')
result = vqe_eig.smallest(H, qc, initial_params, vqe,
ansatz_, samples,
callback=callback, max_meas=max_meas)
result.correct = eig
return result
def simulate(ansatz_name, size, hamiltonian_idx, samples, n_calls,
repeats, h, eig):
intervall = [(-3.0, 3.0)]*(size-1)
H, qc, ansatz_,_ = core.interface.create_and_convert(ansatz_name, h)
vqe = vqe_bayes(n_calls=n_calls)
result = vqe_eig.smallest(H, qc, intervall, vqe, ansatz_, samples,
return_all=True)
result.correct = eig
return result
def simulate(ansatz_name, size, hamiltonian_idx, samples, n_calls, repeats, h,
eig):
# TODO: create VQE-object here! (not multiprocess safe)
# TODO: run e.g. smallest here and return result.
interval = [(-1.0, 1.0)] * (size - 1)
H, qc, ansatz_, _ = core.interface.create_and_convert(ansatz_name, h)
vqe = vqe_bayes(n_calls=n_calls)
result = vqe_eig.smallest(H, qc, interval, vqe, ansatz_, samples)
result.correct = eig
return result
def simulate(size, hamiltonian_idx, V, max_meas, samples, h, eig):
H, qc, ansatz_, initial_params = \
core.interface.create_and_convert('multi_particle', h)
vqe = vqe_nelder_mead(samples=samples, H=H, fatol=0, xatol=0)
tol_para = 1e-3
max_same_para = 3
callback = cb.restart(max_same_para, tol_para)
result = vqe_eig.smallest(H, qc, initial_params, vqe,
ansatz_, samples,
callback=callback, max_meas=max_meas)
result.correct = eig
return result
def simulate(ansatz_name, size, hamiltonian_idx, samples, repeats, h, eig):
# TODO: create VQE-object here! (not multiprocess safe)
# TODO: run e.g. smallest here and return result.
H, qc, ansatz_, initial_params = core.interface.create_and_convert(
ansatz_name, h)
vqe = vqe_nelder_mead(samples=samples, H=H)
max_fun_evals = 200
result = vqe_eig.smallest(H,
qc,
initial_params,
vqe,
ansatz_,
samples,
max_meas=samples * max_fun_evals)
result.correct = eig
return result
def simulate(size, hamiltonian_idx, V, max_meas, samples, h, eig):
H, qc, ansatz_, initial_params = \
core.interface.create_and_convert('multi_particle', h)
n_calls = int(round(max_meas / samples))
vqe = vqe_bayes(n_calls=n_calls)
def callback(*args, **kwargs):
pass
initial_params = [(-1.0, 1.0)] * (size - 1)
result = vqe_eig.smallest(H,
qc,
initial_params,
vqe,
ansatz_,
samples,
callback=callback)
result.correct = eig
return result
from pyquil import get_qc
from core import vqe_eig
from core import ansatz
from core import callback as cb
import numpy as np
from core import matrix_to_op
samples = 5000
sweep_params = 30
qc = get_qc('3q-qvm')
j, V = 1, 1
h = lipkin_quasi_spin.hamiltonian(j, V)[0]
# plot = liveplot.Liveplot()
# vqe_analysis.sweep_parameters(h, qc, new_version=True, samples=None,
# num_para=sweep_params, start=-3, stop=3, callback=
# plot.plotline, plot=False)
energies = vqe_eig.smallest(matrix_to_op.multi_particle(h),
qc,
init_params.ones(h.shape[0]),
ansatz_=ansatz.multi_particle(h.shape[0]),
samples=samples,
fatol=1e-1 * 16 / np.sqrt(samples),
return_all_data=True,
callback=cb.merge_callbacks(
cb.scatter(), cb.stop_if_stuck_x_times(2)))
print(energies)
