diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..ad2c176421fcd4f8a51bb51f2c1b1bf60b04fb23
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+data/
+*.ipynb
+
diff --git a/plot/bell_plotting_helpers.py b/plot/bell_plotting_helpers.py
index 1fd34a21ebef2622c29fb04d55f1f2636aef1c2e..504284765f1ef17b238646374a7479a2eaf4285c 100644
--- a/plot/bell_plotting_helpers.py
+++ b/plot/bell_plotting_helpers.py
@@ -1256,7 +1256,7 @@ def errors_sym_vs_asym(p_eg = 0.0,
errors_5[i][j] = (bell_universal_new(p_ge = p_ge_range[i],
before_measure_flip_probability=p_x_err_range[j],
after_clifford_depolarization= after_clifford_depolarization,
- after_reset_flip_probability = after_reset_flip_probability,
+ after_s17reset_flip_probability = after_reset_flip_probability,
before_round_data_depolarization=before_round_data_depolarization,
cycles = cycles, rounds=3, parity=parity, phase=phase, runs = runs,
ancilla_fb=True, ancilla_fb_adaptive=True, ancilla_multiple=True, break_experiment= break_experiment)/runs)
diff --git a/plot/s17_plotting_helpers.py b/plot/s17_plotting_helpers.py
index f99ae06906357786ed486e05cae8e591f644c09e..d81ba2246a9ab4737b2f648dfe4926e823cd9887 100644
--- a/plot/s17_plotting_helpers.py
+++ b/plot/s17_plotting_helpers.py
@@ -3,7 +3,9 @@ import numpy as np
import random
import matplotlib.pyplot as plt
import scipy
+import pymatching
+import json
# amplitude decay probability before measurement.
@@ -11,7 +13,8 @@ import scipy
def simulate_circuit(circuit_in,
p_amp_ge = 0.01,
ancilla_fb = False,
- after_clifford_depolarization_2 = 0.01):
+ after_clifford_depolarization_2 = 0.01,
+ active_reset = True):
### Operator string.
# qb: 1, 3, 5
logical_qubits = [1,3,5] # qubits of logical operator on S17
@@ -97,6 +100,7 @@ def simulate_circuit(circuit_in,
if len(x_ancillas_dict[qubit]['msmt_last']) > 3:
x_ancillas_dict[qubit]['msmt_last'].pop(0)
+ # 2-qubit gates different error probabilities.
elif instruction.name == 'DEPOLARIZE2' :
ts = instruction.targets_copy()
qubits = [q.value for q in ts]
@@ -108,7 +112,6 @@ def simulate_circuit(circuit_in,
return simulator
-import pymatching
def count_logical_errors(circuit: stim.Circuit, num_shots: int,
p_amp_ge = 0.01, ancilla_fb = False, after_clifford_depolarization_2 =0.01) -> int:
@@ -144,6 +147,145 @@ def count_logical_errors(circuit: stim.Circuit, num_shots: int,
return num_errors
import scipy
+
+
+
+def plot_data_S17_rounds(decoding_rounds = 22,
+ after_clifford_depolarization=0.001,
+ after_clifford_depolarization_2 = 0.01,
+ after_reset_flip_probability=0.00,
+ before_round_data_depolarization=0.00,
+ before_measure_flip_probability = 0.00,
+ p_ge=0.01,
+ runs=100,
+ plot_from_data_dict = False,
+ data_dict = None
+):
+ if not plot_from_data_dict:
+ data_dict = {}
+
+ # No amplitude damping, no ancilla feedback
+ if not plot_from_data_dict:
+ rounds_array, logical_err_array = data_S17_rounds(decoding_rounds = decoding_rounds,
+ after_clifford_depolarization= after_clifford_depolarization,
+ after_clifford_depolarization_2 = after_clifford_depolarization_2,
+ after_reset_flip_probability = after_reset_flip_probability,
+ before_measure_flip_probability = before_measure_flip_probability,
+ before_round_data_depolarization=before_round_data_depolarization,
+ p_ge = 0,
+ runs = runs,
+ ancilla_fb = False)
+ data_dict['no_amp_no_fb'] = {}
+ data_dict['no_amp_no_fb']['rounds_array'] = list(rounds_array)
+ data_dict['no_amp_no_fb']['logical_err_array'] = logical_err_array
+ else:
+ rounds_array = data_dict['no_amp_no_fb']['rounds_array']
+ logical_err_array = data_dict['no_amp_no_fb']['logical_err_array']
+
+ param, vars = scipy.optimize.curve_fit(lambda t,b: 1-np.exp(-b*t), rounds_array, logical_err_array)
+ plt.plot(rounds_array,logical_err_array, label= rf'raw: $p_{{ge}}:${0}', color='r', marker='o')
+ plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: $ p_{{ge}}:${0} ($T_1$:{ round(1/param[0],1)})',\
+ linestyle = 'dotted', color='r', marker = 'x' )
+
+ # With amplitude damping, no ancilla feedback
+ if not plot_from_data_dict:
+ rounds_array, logical_err_array = data_S17_rounds(decoding_rounds = decoding_rounds,
+ after_clifford_depolarization= after_clifford_depolarization,
+ after_clifford_depolarization_2 = after_clifford_depolarization_2,
+ after_reset_flip_probability = after_reset_flip_probability,
+ before_measure_flip_probability = before_measure_flip_probability,
+ before_round_data_depolarization=before_round_data_depolarization,
+ p_ge = p_ge,
+ runs = runs,
+ ancilla_fb = False)
+ data_dict['with_amp_no_fb'] = {}
+ data_dict['with_amp_no_fb']['rounds_array'] = list(rounds_array)
+ data_dict['with_amp_no_fb']['logical_err_array'] = logical_err_array
+ else:
+ rounds_array = data_dict['with_amp_no_fb']['rounds_array']
+ logical_err_array = data_dict['with_amp_no_fb']['logical_err_array']
+
+ param, vars = scipy.optimize.curve_fit(lambda t,b: 1-np.exp(-b*t), rounds_array, logical_err_array)
+ plt.plot(rounds_array,logical_err_array, label=rf'raw: no fb, $p_{{ge}}:${p_ge}', color='b', marker='o')
+ plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: no fb, $p_{{ge}}:${p_ge} ($T_1$:{ round(1/param[0],1)})',\
+ linestyle = 'dotted',color='b',marker = 'x' )
+
+ # With amplitude damping, with ancilla feedback
+ if not plot_from_data_dict:
+ rounds_array, logical_err_array = data_S17_rounds(decoding_rounds = decoding_rounds,
+ after_clifford_depolarization= after_clifford_depolarization,
+ after_clifford_depolarization_2 = after_clifford_depolarization_2,
+ after_reset_flip_probability = after_reset_flip_probability,
+ before_measure_flip_probability = before_measure_flip_probability,
+ before_round_data_depolarization=before_round_data_depolarization,
+ p_ge = p_ge,
+ runs = runs,
+ ancilla_fb = True)
+ data_dict['with_amp_with_fb'] = {}
+ data_dict['with_amp_with_fb']['rounds_array'] = list(rounds_array)
+ data_dict['with_amp_with_fb']['logical_err_array'] = logical_err_array
+ else:
+ rounds_array = data_dict['with_amp_with_fb']['rounds_array']
+ logical_err_array = data_dict['with_amp_with_fb']['logical_err_array']
+
+ param, vars = scipy.optimize.curve_fit(lambda t,b: 1-np.exp(-b*t), rounds_array, logical_err_array)
+ plt.plot(rounds_array,logical_err_array, label=rf'raw: fb, $p_{{ge}}:${p_ge}', color='g', marker='.')
+ plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: fb, $p_{{ge}}:${p_ge} ($T_1$:{ round(1/param[0],1)})',\
+ linestyle = 'dotted',color='g',marker = 'x' )
+
+ # Store data:
+ print('data_dict', data_dict)
+ if not plot_from_data_dict:
+ with open(f'data/S17_rounds{decoding_rounds-1}_peg{p_ge}_polerr{after_clifford_depolarization_2}_rep{runs}.txt', 'w') as f:
+ json.dump(data_dict, f, indent=4)
+ print(f'wrote data to file data/S17_rounds{decoding_rounds-1}_peg{p_ge}_polerr{after_clifford_depolarization_2}_rep{runs}.txt')
+
+ plt.legend( loc='upper left', borderaxespad=0)
+ plt.title(rf' S17, logical error probability $E_L$ (for $|0\rangle_L$), $p_{{ge}}$: {p_ge}, $p_{{2qgate}}$: {after_clifford_depolarization_2}')
+ plt.xlabel('rounds')
+ plt.ylabel('logical error probability $E_L$ ')
+ plt.ylim([0,0.6])
+ plt.savefig(f'data/S17_rounds{decoding_rounds-1}_peg{p_ge}_polerr{after_clifford_depolarization_2}_rep{runs}.png')
+ print(f'saved plot to data/S17_rounds{decoding_rounds-1}_peg{p_ge}_polerr{after_clifford_depolarization_2}_rep{runs}.png')
+ plt.show()
+
+def data_S17_rounds(decoding_rounds = 3,
+ after_clifford_depolarization= 0.001,
+ after_clifford_depolarization_2 = 0.01,
+ after_reset_flip_probability = 0.001,
+ before_measure_flip_probability = 0.001,
+ before_round_data_depolarization=0.01,
+ p_ge = 0.5,
+ runs = 1000,
+ ancilla_fb = False):
+
+
+ logical_err_array = []
+
+ rounds_array = range(3, decoding_rounds, 3)
+ for rounds in rounds_array:
+ surface_code_circuit = stim.Circuit.generated(
+ "surface_code:rotated_memory_z",
+ rounds=rounds,
+ distance=3,
+ after_clifford_depolarization= after_clifford_depolarization,
+ after_reset_flip_probability=after_reset_flip_probability,
+ before_measure_flip_probability= before_measure_flip_probability,
+ before_round_data_depolarization=before_round_data_depolarization
+ )
+
+ n_errors = count_logical_errors(surface_code_circuit,
+ runs,
+ p_amp_ge=p_ge,
+ ancilla_fb = ancilla_fb,
+ after_clifford_depolarization_2= after_clifford_depolarization_2)
+ logical_err = n_errors/runs
+ logical_err_array.append(logical_err)
+ return rounds_array, logical_err_array
+
+
+
+'''
def plot_data_S17(decoding_rounds = 3,
after_clifford_depolarization= 0.01,
after_clifford_depolarization_2 = 0.01,
@@ -192,71 +334,4 @@ def plot_data_S17(decoding_rounds = 3,
plt.ylabel('p_error')
return p_amp_ge_array, logical_err_array
-
-
-
-
-def plot_data_S17_rounds(decoding_rounds = 3,
- after_clifford_depolarization= 0.001,
- after_clifford_depolarization_2 = 0.01,
- after_reset_flip_probability = 0.001,
- before_measure_flip_probability = 0.001,
- before_round_data_depolarization=0.01,
- n_points = 50, max_err = 0.5, runs = 1000, plot = True,
- ancilla_fb = False):
-
-
- logical_err_array = []
-
- rounds_array = range(3, decoding_rounds, 3)
- for rounds in rounds_array:
- surface_code_circuit = stim.Circuit.generated(
- "surface_code:rotated_memory_z",
- rounds=rounds,
- distance=3,
- after_clifford_depolarization= after_clifford_depolarization,
- after_reset_flip_probability=after_reset_flip_probability,
- before_measure_flip_probability= before_measure_flip_probability,
- before_round_data_depolarization=before_round_data_depolarization
- )
-
- n_errors = count_logical_errors(surface_code_circuit,
- runs,
- p_amp_ge=max_err,
- ancilla_fb = ancilla_fb,
- after_clifford_depolarization_2= after_clifford_depolarization_2)
- logical_err = n_errors/runs
- logical_err_array.append(logical_err)
-
- if plot:
- param, vars = scipy.optimize.curve_fit(lambda t,b: 1-np.exp(-b*t), rounds_array, logical_err_array)
- print(param)
- if max_err == 0 and not ancilla_fb:
- plt.plot(rounds_array,logical_err_array, label= rf'raw: $p_{{ge}}:${max_err}', color='r', marker='o')
- plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: $ p_{{ge}}:${max_err} ($T_1$:{ round(1/param[0],1)})',\
- linestyle = 'dotted', color='r', marker = 'x' )
-
- elif max_err == 0 and ancilla_fb:
- plt.plot(rounds_array,logical_err_array, label=rf'raw: fb, $p_{{ge}}:${max_err}', color='g', marker='o')
- plt.plot(rounds_array,1-np.exp(-param[0]*rounds_array),label= rf'fit: fb, $p_{{ge}}:${max_err} ($T_1$:{ round(1/param[0],1)})' ,\
- linestyle = 'dotted',color='g',marker = 'x' )
-
- elif ancilla_fb:
- plt.plot(rounds_array,logical_err_array, label=rf'raw: fb, $p_{{ge}}:${max_err}', color='b', marker='o')
- plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: fb, $p_{{ge}}:${max_err} ($T_1$:{ round(1/param[0],1)})',\
- linestyle = 'dotted',color='b',marker = 'x' )
-
- else:
- plt.plot(rounds_array,logical_err_array, label=rf'raw: $p_{{ge}}:${max_err}', color='k', marker='.')
- plt.plot(rounds_array, 1-np.exp(-param[0]*rounds_array),label= rf'fit: $p_{{ge}}:${max_err} ($T_1$:{ round(1/param[0],1)})',\
- linestyle = 'dotted',color='k',marker = 'x' )
-
- plt.legend( loc='upper left', borderaxespad=0)
- # plt.title(f' S17 Logical Z-error on vs. p_eg on ancillas (dec. rnds: {decoding_rounds}, depol gate err: {after_clifford_depolarization}, p_eg: {max_err} \
- # ,data_err before rnd: {before_round_data_depolarization}, after_reset_flip_probability; {after_reset_flip_probability},before_measure_flip_probability:\
- # {before_measure_flip_probability} runs: {runs})')
- plt.title(rf' S17, logical error probability $E_L$ (for $|0\rangle_L$), $p_{{ge}}$: {max_err}')
- plt.xlabel('rounds')
- plt.ylabel('logical error probability $E_L$ ')
-
- return rounds_array, logical_err_array
\ No newline at end of file
+'''
\ No newline at end of file