def circuit_syndrome_test():
"""
compares syndromes generated by measurement circuit with q = 0 to
those generated by perfect stabiliser measurement from 2D
simulation.
They all ought to match.
"""
two_d_sim = dc2.Sim2D(15, 15, 0.00)
three_d_sim = dc3.Sim3D(15, 1000, ('pq', 0.1, 0.0))
err_hist, synd_hist = three_d_sim.history()
for idx, err in enumerate(err_hist):
idl_x_synd, idl_z_synd = two_d_sim.syndromes(err)
assert synd_hist['X'][idx] == set(idl_x_synd)
assert synd_hist['Z'][idx] == set(idl_z_synd)
def run_batch(err_lo,
err_hi,
n_points,
dists,
n_trials,
flnm,
sim_type='iidxz'):
"""
Makes a bunch of simulation objects and runs them, based on input
parameters.
"""
sim_type = sim_type.lower()
if sim_type not in ['iidxz', 'pq', 'circ']:
raise ValueError("sim_type must be one of: ['iidxz', 'pq', 'circ']")
errs = np.linspace(err_lo, err_hi, n_points)
output_dict = locals()
for dist in dists:
failures = []
for err in errs:
if sim_type == 'iidxz':
current_sim = dc2.Sim2D(dist, dist, err)
elif sim_type == 'pq':
current_sim = dc3.Sim3D(dist, dist, ('pq', err, err))
elif sim_type == 'circ':
raise NotImplementedError("Coming Soon!")
current_sim.run(n_trials, progress=False)
failures.append(current_sim.errors)
output_dict.update({'failures ' + str(dist): failures})
with open(flnm, 'wb') as phil:
pkl.dump(output_dict, phil)
pass
def calc_parity_3d(dist, meas_rnds, model, use_blossom):
"""
Calculates the amount of errors after d faulty Surface
code rounds mod 2. Based on the result we know the logical
state of the logical qubit
"""
#dq = [1,2,3,7,8,9,13,14,15]
no_anc = (d + 1) * (d - 1) / 2
flp = [0] * no_anc
#flips_X = flp * (d+1)
flips_Z = [0] * (d + 1)
for i in range(d + 1):
flips_Z[i] = deepcopy(flp)
sim = dc3.Sim3D(dist, meas_rnds, model, use_blossom)
err_history, syndrome_history = sim.history()
#print('err_history ', err_history)
#print('syndrome_history ', syndrome_history)
#print("-----------")
#print(err_history[2].x_set)
#print(err_history[2].z_set)
#print("-----------")
#print(len(err_history[2].x_set))
#print(len(err_history[2].z_set))
#print("-----------")
#print(len(err_history[2].x_set) % 2)
#print(len(err_history[2].z_set) % 2)
#print("-----------")
x_parity = len(err_history[2].x_set) % 2
#z_parity = len(err_history[2].z_set) % 2
if x_parity == 1:
log_err = 'X'
else:
log_err = 'I'
#if x_parity == 0:
# log_err = 'I' if z_parity == 0 else 'Z'
#else:
# log_err = 'X' if z_parity == 0 else 'Y'
#print('log_err ', log_err)
#print(syndrome_history.items())
#sim_test = dc.Sim2D(3, 3, 0.01)
#test_layout = SCLayoutClass.SCLayout(3)
#print(sim_test.layout.map)
#print(sim_test.layout.map.inv)
#x_ancs = sim_test.layout.x_ancs()
#z_ancs = sim_test.layout.z_ancs()
#errors_X = ''
#errors_Z = ''
#for i in range(len(dq)):
# errors_X += ('1' if dq[i] in err_history[2].x_set else '0') + ' '
# errors_Z += ('1' if dq[i] in err_history[2].z_set else '0') + ' '
#if d == 3:
#x_ancs = [4,6,10,12]
#z_ancs = [0,5,11,16]
#elif d == 5:
#x_ancs = [7,9,11,17,19,21,27,29,31,37,39,41]
z_ancs = [0, 1, 8, 10, 18, 20, 28, 30, 38, 40, 47, 48]
'''
for j in range(4):
for i in range(4):
if (x_ancs[i] in syndrome_history['X'][j] and x_ancs[i] in syndrome_history['X'][j+1]) or \
(x_ancs[i] not in syndrome_history['X'][j] and x_ancs[i] not in syndrome_history['X'][j+1]):
flips_X[j][i] = 0
else:
flips_X[j][i] = 1
'''
#print(flips_Z)
for j in range(d + 1):
for i in range(no_anc):
if (z_ancs[i] in syndrome_history['Z'][j] and z_ancs[i] in syndrome_history['Z'][j+1]) or \
(z_ancs[i] not in syndrome_history['Z'][j] and z_ancs[i] not in syndrome_history['Z'][j+1]):
flips_Z[j][i] = 0
else:
flips_Z[j][i] = 1
return flips_Z, log_err # flips_X, [errors_X, errors_Z]#, [x_parity, z_parity]
# dist_7_single_sample()
#dumbest_dataset(5, 0.08, 1000000, 'd_5_dataset_million')
d = 3
ms_rnd = 3
no_anc = int((d + 1) * (d - 1) / 2)
p = 0.04
q = 0.04
samples = {}
z_ancs = [0, 5, 11, 16]
flp = [0] * no_anc
flips_Z = [0] * (d + 1)
for i in range(d + 1):
flips_Z[i] = deepcopy(flp)
no_samples = 10000
sim_test2d = dc2.Sim2D(d, d, p)
sim_test = dc3.Sim3D(d, d, ('fowler', p), True)
#err = sim_test.run(no_samples, progress=False, metric=None, bdy_info=None, final_perfect_rnd=True)
#print(err)
#calc_parity_3d(dist, meas_rnds, model, use_blossom)
for cycl in range(1000000):
err, synd = sim_test.history(True)
rnd_err = err[2].x_set
rd_er = sp.Pauli(rnd_err, [])
corr_blossom = sim_test.correction(synd, metric=None, bdy_info=None)
mwpm_log_err = sim_test.logical_error(err[-1], corr_blossom)
synds_2d = sim_test2d.syndromes(rd_er)
dumb_x_corr, dumb_z_corr = sim_test2d.dumb_correction(synds_2d, False)
dumb_log_err = sim_test2d.logical_error(rd_er, dumb_x_corr,
dumb_z_corr)
import decoding_2d as dc2
import decoding_3d as dc3
import error_model as em
from functools import reduce
import sparse_pauli as sp
import tensorflow as tf
import numpy as np
import time
from datetime import timedelta
import random
d = 3
p=0.08
iterations = 5000
sim_test2d = dc2.Sim2D(d, d, p)
sim_test = dc3.Sim3D(d, d, ('pq', p, p), True)
z_ancs_keys = list(sim_test.layout.z_ancs())
logicals = sim_test.layout.logicals()
cycles = 0
mwpm_log_err = 0
plut_log_err = 0
mwpm = 0
dumb = 0
nn = 0
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.05, shape=shape)
def run_batch(err_lo,
err_hi,
n_points,
dists,
n_trials,
flnm,
sim_type='iidxz',
bc='rotated',
bp=False):
"""
Makes a bunch of simulation objects and runs them, based on input
parameters.
"""
sim_type = sim_type.lower()
if sim_type not in ['iidxz', 'dep', 'pq', 'circ']:
raise ValueError(
"sim_type must be one of: ['iidxz', 'dep', 'pq', 'circ']")
errs = np.linspace(err_lo, err_hi, n_points)
output_dict = locals()
for dist in dists:
failures = []
for err in errs:
if sim_type in ['iidxz', 'dep']:
# current_sim = dc2.Sim2D(dist, dist, err, useBlossom=False, boundary_conditions=bc)
# dist_func = fancy_dist(dist, err, bc=bc)
# dist_func = entropic_dist(dist, err)
if bc == 'open':
current_sim = dc2.Sim2D(dist,
dist,
err,
useBlossom=True,
boundary_conditions=bc)
else:
current_sim = dc2.Sim2D(dist,
dist,
err,
useBlossom=True,
boundary_conditions=bc)
elif sim_type == 'pq':
current_sim = dc3.Sim3D(dist, dist, ('pq', err, err))
elif sim_type == 'circ':
current_sim = dc3.Sim3D(dist, dist, ('fowler', err))
met_fun = dc3.nest_metric(current_sim, err)
bdy_fun = dc3.nest_bdy_tpl(current_sim, err)
if sim_type == 'dep':
current_sim.error_model = em.depolarize(
err, [[current_sim.layout.map[_]]
for _ in current_sim.layout.datas])
# dist_func = fancy_dist(dist, 0.66666667 * err)
current_sim.run(n_trials, progress=False, bp=bp)
# current_sim.run(n_trials, progress=False, bp=bp, bp_rounds=0)
# current_sim.run(n_trials, progress=False,
# metric=met_fun, bdy_info=bdy_fun)
# current_sim.run(n_trials, progress=False, dist_func=dist_func)
# hb.bravyi_run(current_sim, n_trials)
failures.append(current_sim.errors)
output_dict.update({'failures ' + str(dist): failures})
with open(flnm, 'wb') as phil:
pkl.dump(output_dict, phil)
pass
