def run_sim_repetitions_stage3(aht,
ht,
zz,
bond_length,
n_repetitions,
exact=True,
noisy=False):
"""Executes state preparation circuit multiple times and computes the energy expectation
over n times (n_repetitions).
Args:
=====
aht, ht, zz : numeric
Parameters for decoding circuit
bond_length : float
Bond length
n_repetitions : int
Number of circuit runs
exact : bool
If True, works with wavefunction
noisy : bool
If True, runs noisy version of circuit
Returns:
========
energy_expectation : float
Energy expectation value
"""
if exact == True and noisy == False:
final_state = _run_sim_stage3(aht,
ht,
zz,
exact=True,
print_circuit=False,
noisy=False)
energy_expectation = settings.compute_energy_expectation(
bond_length, particle_number_conserve(final_state))
return energy_expectation
energy_expectation = 0
for k in range(n_repetitions):
final_state = _run_sim_stage3(aht,
ht,
zz,
exact,
print_circuit=False,
noisy=noisy)
energy_expectation += settings.compute_energy_expectation(
bond_length,
particle_number_conserve(final_state)) / float(n_repetitions)
return float(energy_expectation)
def one_run(alpha, bond_length):
f_state = _run_sim_stage1(alpha=alpha,
exact=True,
print_circuit=False,
noisy=True)
return settings.compute_energy_expectation(
bond_length, particle_number_conserve(f_state))
def compute_stage1_cost_function(alpha,
bond_length,
n_repetitions=100,
exact=True,
noisy=False):
"""Executes state preparation circuit multiple times and computes the energy expectation
over n times (n_repetitions).
Args:
=====
alpha : numeric
Parameter for state preparation circuit
bond_length : float
Bond length
n_repetitions : int
Number of circuit runs
exact : bool
If True, works with wavefunction
noisy : bool
If True, runs noisy version of circuit
Returns:
========
energy_expectation : float
Energy expectation value
"""
if exact is True and noisy is False:
f_state = _run_sim_stage1(alpha=alpha,
exact=exact,
print_circuit=False,
noisy=noisy)
energy_expectation = settings.compute_energy_expectation(
bond_length, particle_number_conserve(f_state))
return energy_expectation
energy_expectation = 0
for k in range(int(n_repetitions)):
f_state = _run_sim_stage1(alpha=alpha,
exact=exact,
print_circuit=False,
noisy=noisy)
energy_expectation += settings.compute_energy_expectation(
bond_length,
particle_number_conserve(f_state)) / float(n_repetitions)
return energy_expectation
def run_sim_repetitions_stage3(aht,
ht,
zz,
bond_length,
n_repetitions,
exact=True,
noisy=False):
"""Executes state preparation circuit multiple times and computes the energy expectation
over n times (n_repetitions).
Args:
=====
aht, ht, zz : numeric
Parameters for decoding circuit
bond_length : float
Bond length
n_repetitions : int
Number of circuit runs
exact : bool
If True, works with wavefunction
noisy : bool
If True, runs noisy version of circuit
Returns:
========
energy_expectation : float
Energy expectation value
"""
if exact == True and noisy == False:
final_state = _run_sim_stage3(aht,
ht,
zz,
exact=True,
print_circuit=False,
noisy=False)
energy_expectation = settings.compute_energy_expectation(
bond_length, particle_number_conserve(final_state))
return energy_expectation
# energy_expectation = 0
# for k in range(n_repetitions):
# energy_expectation += one_run(aht,ht,zz,bond_length)
# energy_expectation = energy_expectation / float(n_repetitions)
p = Pool()
args = [(aht, ht, zz, bond_length)] * n_repetitions
results = p.starmap(one_run, args)
energy_expectation = np.array(results).mean()
p.close()
return energy_expectation