7. 新功能展示(任意控制脉冲，耦合比特操作)¶
因各量子计算机参数不同，本demo所使用的参数范围，在不同机器上可能无法得到相同的扫描结果。
1D Demo¶
In [2]:
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# q-q swap, Q50, Q45
import numpy as np
qubit0 = 'Q50'
qubit1 = 'Q45'
coupler = 'G82'
qBiasStartAmp = 700
qBiasEndAmp = 1000
qBiasStep = 5
qBiasAmp = np.arange(qBiasStartAmp, qBiasEndAmp, qBiasStep)
template1 = '''
X {qubit0}
I {qubit0} 10
B {qubit0} {coupler}
PLS {qubit0} 1 -1 {pulseLength} {qBiasAmplitude} 0 0 0 4
G {coupler} {pulseLength} {g}
I {qubit0} {pulseLength}
I {qubit0} 10
B {qubit0} {qubit1}
M {qubit1}
'''
pulseLength = 1000;
g = -10e6;
template = template1
qCircuits = []
for qBias in qBiasAmp:
c = template.format(qubit0 = qubit0, qubit1 = qubit1, coupler = coupler, pulseLength = pulseLength, qBiasAmplitude = qBias, g = g)
qCircuits.append(c)
print(len(qCircuits))
print(qCircuits[0])
# q-q swap, Q50, Q45
import numpy as np
qubit0 = 'Q50'
qubit1 = 'Q45'
coupler = 'G82'
qBiasStartAmp = 700
qBiasEndAmp = 1000
qBiasStep = 5
qBiasAmp = np.arange(qBiasStartAmp, qBiasEndAmp, qBiasStep)
template1 = '''
X {qubit0}
I {qubit0} 10
B {qubit0} {coupler}
PLS {qubit0} 1 -1 {pulseLength} {qBiasAmplitude} 0 0 0 4
G {coupler} {pulseLength} {g}
I {qubit0} {pulseLength}
I {qubit0} 10
B {qubit0} {qubit1}
M {qubit1}
'''
pulseLength = 1000;
g = -10e6;
template = template1
qCircuits = []
for qBias in qBiasAmp:
c = template.format(qubit0 = qubit0, qubit1 = qubit1, coupler = coupler, pulseLength = pulseLength, qBiasAmplitude = qBias, g = g)
qCircuits.append(c)
print(len(qCircuits))
print(qCircuits[0])
60 X Q50 I Q50 10 B Q50 G82 PLS Q50 1 -1 1000 700 0 0 0 4 G G82 1000 -10000000.0 I Q50 1000 I Q50 10 B Q50 Q45 M Q45
In [3]:
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from ezQgd import *
import math
import time
account = Account(login_key='2p1adksE+s6ib6uJa5/P/G3sc2O9kejgGBjgIukfGmY=', machine_name='gd_qc1')
# 提交之前先下载使用的实验参数
config_json = account.download_config(down_file=False)
exp_name = "qq_swap_{qubit0}_{qubit1}_".format(qubit0 = qubit0, qubit1 = qubit1)+str(int(time.time()))
query_id = account.submit_job(circuit=qCircuits, version=exp_name, num_shots=2000, is_verify=False)
# 分批查询实验结果，批量返回的只有原始数据，后端最多支持50条实验id批量查询
p1 = []
if query_id:
for idx in range(0, len(query_id), 10):
print(query_id[idx:idx+10])
result=account.query_experiment(query_id[idx:idx+10], max_wait_time=60*1000)
for query_res in result:
# 实验结果转换，概率转换，读取矫正
# results = account.readout_data_to_state_probabilities_whole(query_res)
prob = account.probability_calibration(query_res, config_json)
prob = account.probability_correction(prob)
p1.append(prob['1'])
import matplotlib.pyplot as plt
plt.plot(qBiasAmp, p1)
plt.xlabel('qubit0 bias(a.u.)')
plt.ylabel('qubit1 |1> probability')
plt.title('q-q swap')
plt.show()
from ezQgd import *
import math
import time
account = Account(login_key='2p1adksE+s6ib6uJa5/P/G3sc2O9kejgGBjgIukfGmY=', machine_name='gd_qc1')
# 提交之前先下载使用的实验参数
config_json = account.download_config(down_file=False)
exp_name = "qq_swap_{qubit0}_{qubit1}_".format(qubit0 = qubit0, qubit1 = qubit1)+str(int(time.time()))
query_id = account.submit_job(circuit=qCircuits, version=exp_name, num_shots=2000, is_verify=False)
# 分批查询实验结果，批量返回的只有原始数据，后端最多支持50条实验id批量查询
p1 = []
if query_id:
for idx in range(0, len(query_id), 10):
print(query_id[idx:idx+10])
result=account.query_experiment(query_id[idx:idx+10], max_wait_time=60*1000)
for query_res in result:
# 实验结果转换，概率转换，读取矫正
# results = account.readout_data_to_state_probabilities_whole(query_res)
prob = account.probability_calibration(query_res, config_json)
prob = account.probability_correction(prob)
p1.append(prob['1'])
import matplotlib.pyplot as plt
plt.plot(qBiasAmp, p1)
plt.xlabel('qubit0 bias(a.u.)')
plt.ylabel('qubit1 |1> probability')
plt.title('q-q swap')
plt.show()
['7080735326762696705', '7080735326762696707', '7080735326762696709', '7080735326762696711', '7080735326762696713', '7080735326762696715', '7080735326762696717', '7080735326762696719', '7080735326762696721', '7080735326762696723'] 查询实验结果请等待: 3.59秒 查询实验结果请等待: 4.06秒 查询实验结果请等待: 2.86秒 查询实验结果请等待: 3.48秒 查询实验结果请等待: 5.66秒 查询实验结果请等待: 5.49秒 查询实验结果请等待: 3.02秒 查询实验结果请等待: 3.74秒 ['7080735326762696725', '7080735326762696727', '7080735326766891008', '7080735326766891010', '7080735326766891012', '7080735326766891014', '7080735326766891016', '7080735326766891018', '7080735326766891020', '7080735326766891022'] ['7080735326766891024', '7080735326766891026', '7080735326766891028', '7080735326766891030', '7080735326766891032', '7080735326766891034', '7080735326766891036', '7080735326766891038', '7080735326766891040', '7080735326766891042'] ['7080735326766891044', '7080735326766891046', '7080735326766891048', '7080735326766891050', '7080735326766891052', '7080735326766891054', '7080735326766891056', '7080735326766891058', '7080735326766891060', '7080735326766891062'] ['7080735326766891064', '7080735326766891066', '7080735326766891068', '7080735326766891070', '7080735326766891072', '7080735326766891074', '7080735326766891076', '7080735326771085312', '7080735326771085314', '7080735326771085316'] ['7080735326771085318', '7080735326771085320', '7080735326771085322', '7080735326771085324', '7080735326771085326', '7080735326771085328', '7080735326771085330', '7080735326771085332', '7080735326771085334', '7080735326771085336']
2D Demo¶
In [1]:
Copied!
# q-q swap, Q50, Q45
import numpy as np
qubit0 = 'Q50'
qubit1 = 'Q45'
coupler = 'G82'
qBiasStartAmp = 725
qBiasEndAmp = 950
qBiasStep = 10
qBiasAmp = np.arange(qBiasStartAmp, qBiasEndAmp, qBiasStep)
durationStart = 100
durationEnd = 2000
durationStep = 100
duration = np.arange(durationStart, durationEnd, durationStep)
numDurations = len(duration)
template1 = '''
X {qubit0}
I {qubit0} 10
B {qubit0} {coupler}
PLS {qubit0} 1 -1 {pulseLength} {qBiasAmplitude} 0 0 0 4
G {coupler} {pulseLength} {g}
I {qubit0} {pulseLength}
I {qubit0} 10
B {qubit0} {qubit1}
M {qubit1}
'''
pulseLength = 1000;
g = -15e6;
template = template1
qCircuits = []
for qBias in qBiasAmp:
for d in duration:
c = template.format(qubit0 = qubit0, qubit1 = qubit1, coupler = coupler, pulseLength = d, qBiasAmplitude = qBias, g = g)
qCircuits.append(c)
print(len(qCircuits))
print(qCircuits[0])
# q-q swap, Q50, Q45
import numpy as np
qubit0 = 'Q50'
qubit1 = 'Q45'
coupler = 'G82'
qBiasStartAmp = 725
qBiasEndAmp = 950
qBiasStep = 10
qBiasAmp = np.arange(qBiasStartAmp, qBiasEndAmp, qBiasStep)
durationStart = 100
durationEnd = 2000
durationStep = 100
duration = np.arange(durationStart, durationEnd, durationStep)
numDurations = len(duration)
template1 = '''
X {qubit0}
I {qubit0} 10
B {qubit0} {coupler}
PLS {qubit0} 1 -1 {pulseLength} {qBiasAmplitude} 0 0 0 4
G {coupler} {pulseLength} {g}
I {qubit0} {pulseLength}
I {qubit0} 10
B {qubit0} {qubit1}
M {qubit1}
'''
pulseLength = 1000;
g = -15e6;
template = template1
qCircuits = []
for qBias in qBiasAmp:
for d in duration:
c = template.format(qubit0 = qubit0, qubit1 = qubit1, coupler = coupler, pulseLength = d, qBiasAmplitude = qBias, g = g)
qCircuits.append(c)
print(len(qCircuits))
print(qCircuits[0])
437 X Q50 I Q50 10 B Q50 G82 PLS Q50 1 -1 100 725 0 0 0 4 G G82 100 -15000000.0 I Q50 100 I Q50 10 B Q50 Q45 M Q45
In [3]:
Copied!
from ezQgd import *
import math
import time
login_key='2p1adksE+s6ib6uJa5/P/G3sc2O9kejgGBjgIukfGmY=', machine_name='gd_qc1'
# 提交之前先下载使用的实验参数
config_json = account.download_config(down_file=False)
exp_name = "qq_swap_{qubit0}_{qubit1}_".format(qubit0 = qubit0, qubit1 = qubit1)+str(int(time.time()))
query_id = account.submit_job(circuit=qCircuits, version=exp_name, num_shots=2000, is_verify=False)
# 分批查询实验结果，批量返回的只有原始数据，后端最多支持50条实验id批量查询
count = 0
p1 = []
p1_ = []
if query_id:
for idx in range(0, len(query_id), 10):
print(query_id[idx:idx+10])
result=account.query_experiment(query_id[idx:idx+10], max_wait_time=60*1000)
for query_res in result:
# 实验结果转换，概率转换，读取矫正
# results = account.readout_data_to_state_probabilities_whole(query_res)
prob = account.probability_calibration(query_res, config_json)
prob = account.probability_correction(prob)
p1_.append(prob['1'])
count = count + 1
if count == numDurations:
p1.append(p1_)
count = 0
p1_ = []
import matplotlib.pyplot as plt
plt.imshow(p1, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.xlabel('duration')
plt.ylabel('qubit bias')
plt.title('q-q swap')
plt.show()
from ezQgd import *
import math
import time
login_key='2p1adksE+s6ib6uJa5/P/G3sc2O9kejgGBjgIukfGmY=', machine_name='gd_qc1'
# 提交之前先下载使用的实验参数
config_json = account.download_config(down_file=False)
exp_name = "qq_swap_{qubit0}_{qubit1}_".format(qubit0 = qubit0, qubit1 = qubit1)+str(int(time.time()))
query_id = account.submit_job(circuit=qCircuits, version=exp_name, num_shots=2000, is_verify=False)
# 分批查询实验结果，批量返回的只有原始数据，后端最多支持50条实验id批量查询
count = 0
p1 = []
p1_ = []
if query_id:
for idx in range(0, len(query_id), 10):
print(query_id[idx:idx+10])
result=account.query_experiment(query_id[idx:idx+10], max_wait_time=60*1000)
for query_res in result:
# 实验结果转换，概率转换，读取矫正
# results = account.readout_data_to_state_probabilities_whole(query_res)
prob = account.probability_calibration(query_res, config_json)
prob = account.probability_correction(prob)
p1_.append(prob['1'])
count = count + 1
if count == numDurations:
p1.append(p1_)
count = 0
p1_ = []
import matplotlib.pyplot as plt
plt.imshow(p1, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.xlabel('duration')
plt.ylabel('qubit bias')
plt.title('q-q swap')
plt.show()
['7080967464804155393', '7080967464804155395', '7080967464804155397', '7080967464804155399', '7080967464804155401', '7080967464804155403', '7080967464804155405', '7080967464804155407', '7080967464804155409', '7080967464804155411'] 查询实验结果请等待: 4.86秒 查询实验结果请等待: 1.72秒 查询实验结果请等待: 5.25秒 查询实验结果请等待: 3.63秒 查询实验结果请等待: 0.53秒 查询实验结果请等待: 2.93秒 查询实验结果请等待: 0.07秒 查询实验结果请等待: 1.34秒 查询实验结果请等待: 1.21秒 查询实验结果请等待: 5.06秒 查询实验结果请等待: 4.96秒 查询实验结果请等待: 3.29秒 查询实验结果请等待: 4.42秒 查询实验结果请等待: 3.80秒 查询实验结果请等待: 3.17秒 查询实验结果请等待: 3.35秒 查询实验结果请等待: 2.19秒 查询实验结果请等待: 4.62秒 查询实验结果请等待: 0.53秒 查询实验结果请等待: 0.66秒 查询实验结果请等待: 3.55秒 查询实验结果请等待: 1.11秒 查询实验结果请等待: 2.54秒 查询实验结果请等待: 2.18秒 查询实验结果请等待: 2.19秒 查询实验结果请等待: 0.94秒 查询实验结果请等待: 1.32秒 查询实验结果请等待: 1.16秒 查询实验结果请等待: 3.01秒 查询实验结果请等待: 4.62秒 查询实验结果请等待: 2.20秒 查询实验结果请等待: 0.55秒 查询实验结果请等待: 1.25秒 查询实验结果请等待: 4.54秒 查询实验结果请等待: 4.08秒 查询实验结果请等待: 2.00秒 查询实验结果请等待: 1.66秒 查询实验结果请等待: 4.29秒 查询实验结果请等待: 2.39秒 查询实验结果请等待: 2.03秒 查询实验结果请等待: 4.75秒 查询实验结果请等待: 2.55秒 查询实验结果请等待: 5.04秒 查询实验结果请等待: 2.88秒 查询实验结果请等待: 1.63秒 查询实验结果请等待: 4.83秒 查询实验结果请等待: 2.79秒 查询实验结果请等待: 4.22秒 查询实验结果请等待: 4.58秒 查询实验结果请等待: 4.96秒 查询实验结果请等待: 0.41秒 查询实验结果请等待: 4.32秒 查询实验结果请等待: 4.15秒 查询实验结果请等待: 1.61秒 查询实验结果请等待: 1.16秒 查询实验结果请等待: 3.40秒 查询实验结果请等待: 0.44秒 查询实验结果请等待: 3.87秒 查询实验结果请等待: 5.24秒 查询实验结果请等待: 1.91秒 查询实验结果请等待: 1.15秒 查询实验结果请等待: 0.92秒 查询实验结果请等待: 3.68秒 查询实验结果请等待: 1.43秒 查询实验结果请等待: 4.42秒 查询实验结果请等待: 2.01秒 查询实验结果请等待: 3.33秒 查询实验结果请等待: 2.00秒 查询实验结果请等待: 2.88秒 查询实验结果请等待: 0.02秒 查询实验结果请等待: 2.32秒 查询实验结果请等待: 3.67秒 查询实验结果请等待: 4.82秒 查询实验结果请等待: 2.51秒 ['7080967464804155413', '7080967464804155415', '7080967464804155417', '7080967464804155419', '7080967464804155421', '7080967464804155423', '7080967464804155425', '7080967464804155427', '7080967464804155429', '7080967464804155431'] ['7080967464804155433', '7080967464804155435', '7080967464804155437', '7080967464804155439', '7080967464804155441', '7080967464808349696', '7080967464808349698', '7080967464808349700', '7080967464808349702', '7080967464808349704'] ['7080967464808349706', '7080967464808349708', '7080967464808349710', '7080967464808349712', '7080967464808349714', '7080967464808349716', '7080967464808349718', '7080967464808349720', '7080967464808349722', '7080967464808349724'] ['7080967464808349726', '7080967464808349728', '7080967464808349730', '7080967464808349732', '7080967464808349734', '7080967464808349736', '7080967464808349738', '7080967464808349740', '7080967464808349742', '7080967464808349744'] ['7080967464808349746', '7080967464808349748', '7080967464808349750', '7080967464808349752', '7080967464808349754', '7080967464808349756', '7080967464808349758', '7080967464808349760', '7080967464808349762', '7080967464808349764'] ['7080967464808349766', '7080967464808349768', '7080967464808349770', '7080967464808349772', '7080967464808349774', '7080967464808349776', '7080967464808349778', '7080967464808349780', '7080967464808349782', '7080967464808349784'] ['7080967464808349786', '7080967464808349788', '7080967464808349790', '7080967464808349792', '7080967464808349794', '7080967464812544000', '7080967464812544002', '7080967464812544004', '7080967464812544006', '7080967464812544008'] ['7080967464812544010', '7080967464812544012', '7080967464812544014', '7080967464812544016', '7080967464812544018', '7080967464812544020', '7080967464812544022', '7080967464812544024', '7080967464812544026', '7080967464812544028'] ['7080967464812544030', '7080967464812544032', '7080967464812544034', '7080967464812544036', '7080967464812544038', '7080967464812544040', '7080967464812544042', '7080967464812544044', '7080967464812544046', '7080967464812544048'] ['7080967464812544050', '7080967464812544052', '7080967464812544054', '7080967464812544056', '7080967464812544058', '7080967464812544060', '7080967464812544062', '7080967464812544064', '7080967464812544066', '7080967464812544068'] ['7080967464812544070', '7080967464812544072', '7080967464812544074', '7080967464812544076', '7080967464812544078', '7080967464812544080', '7080967464812544082', '7080967464812544084', '7080967464812544086', '7080967464812544088'] ['7080967464812544090', '7080967464812544092', '7080967464812544094', '7080967464812544096', '7080967464812544098', '7080967464812544100', '7080967464812544102', '7080967464812544104', '7080967464812544106', '7080967464812544108'] ['7080967464812544110', '7080967464812544112', '7080967464812544114', '7080967464812544116', '7080967464812544118', '7080967464812544120', '7080967464812544122', '7080967464816738304', '7080967464816738306', '7080967464816738308'] ['7080967464816738310', '7080967464816738312', '7080967464816738314', '7080967464816738316', '7080967464816738318', '7080967464816738320', '7080967464816738322', '7080967464816738324', '7080967464816738326', '7080967464816738328'] ['7080967464816738330', '7080967464816738332', '7080967464816738334', '7080967464816738336', '7080967464816738338', '7080967464816738340', '7080967464816738342', '7080967464816738344', '7080967464816738346', '7080967464816738348'] ['7080967464816738350', '7080967464816738352', '7080967464816738354', '7080967464816738356', '7080967464816738358', '7080967464816738360', '7080967464816738362', '7080967464816738364', '7080967464816738366', '7080967464816738368'] ['7080967464816738370', '7080967464816738372', '7080967464816738374', '7080967464816738376', '7080967464816738378', '7080967464816738380', '7080967464816738382', '7080967464816738384', '7080967464816738386', '7080967464816738388'] ['7080967464816738390', '7080967464816738392', '7080967464816738394', '7080967464816738396', '7080967464816738398', '7080967464816738400', '7080967464816738402', '7080967464816738404', '7080967464816738406', '7080967464816738408'] ['7080967464816738410', '7080967464816738412', '7080967464816738414', '7080967464816738416', '7080967464816738418', '7080967464816738420', '7080967464816738422', '7080967464816738424', '7080967464816738426', '7080967464816738428'] ['7080967464820932608', '7080967464820932610', '7080967464820932612', 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