Fluxonium with multi-path coupling(Simultaneous CNOT gate)#
This notebook is similar but not as detail as examples/fluxonium_multipath_coupling/fluxonium_mpcoupling_6q_x.ipynb. Please check that notebook for more info.
This example is based on Nguyen, L. B. et al. Blueprint for a High-Performance Fluxonium Quantum Processor. PRX Quantum 3, 037001 (2022). We simulate a 6 fluxonium qubit system from an underlying periodic lattice. Idling hamiltonian of the system is
\[H(0) = \sum_i H_{\mathrm{f},i} + \sum_{\langle i,j \rangle } H_{ij}.\]
Hamiltonian of single fluxonium is
\[H_{\mathrm{f},i} = 4E_{\mathrm{C},i} n_i^2 + \frac{1}{2} E_{\mathrm{L},i} (\varphi_i +\varphi_{\mathrm{ext},i})^2
-E_{\mathrm{J},i}\cos \left( \varphi_i \right).\]
The coupling terms have the form
\[H_{ij} = J_{\mathrm{C}} n_i n_j - J_{\mathrm{L}} \varphi_i \varphi_j.\]
The couplings are chosen in a way such that the idling \(ZZ\)-crosstalk is almost zero.
[1]:
import jax
import jax.numpy as jnp
import haiku as hk
from supergrad.helper import Evolve
from supergrad.utils import tensor, permute, compute_fidelity_with_1q_rotation_axis
from supergrad.utils.optimize import scipy_minimize, adam_opt
from supergrad.utils.gates import cnot
from supergrad.utils.utility import tree_print
from supergrad.scgraph.graph_mpc_fluxonium_5x5_periodic import CNOTGatePeriodicGraphOpt
truncated_dim = 3 # how many levels we keep after diagonalization
add_random = True # whether to add random deviations to fluxonium parameters ec,ej,el
share_params = True # whether we treat marked parameters as shared when computing gradients
unify_coupling = True # set all couplings to be equal
compensation_option = 'arbit_single' # allow arbitary single-qubit gate compensation
# This is useful for checking how many errors are fundamentally multi-qubit
# This specify one order of the qubits such that the simultaneous gates are [cnot()] * 3
unitary_order = ['q02', 'q03', 'q12', 'q13', 'q22', 'q23']
# instance the quantum processor graph, and choose a subset for time evolution
gate_graph = CNOTGatePeriodicGraphOpt(seed=1)
qubit_subset = gate_graph.subgraph(unitary_order)
opt = 'adam'
evo = Evolve(qubit_subset, truncated_dim, add_random, share_params, unify_coupling, compensation_option)
# Compute the order of qubits in qubit_subset related to unitary_order
qubit_order = [unitary_order.index(key) for key in qubit_subset.sorted_nodes]
# Compute the corresponding target unitary based on the above order
target_unitary = permute(tensor(*([cnot()] * 3)), [2] * len(qubit_subset.nodes), qubit_order)
Let us print the parameters stored in the evo instance. These parameters are parsed from gate_graph. For how we construct this graph, please check examples/fluxonium_multipath_coupling/graph_5x5_periodic.py and the instructions inside.
[2]:
params = evo.all_params
tree_print(params)
{ 'capacitive_coupling_all_unify': {'strength': 0.07225663},
'inductive_coupling_all_unify': {'strength': -0.01256637},
'q02': {'ec': 6.3532393823, 'ej': 25.1103087675, 'el': 6.344787656},
'q02_pulse_rampcos': { 'amp': 0.18128846,
'omega_d': 2.58934559,
'phase': -0.24290228,
't_plateau': 69.93608145,
't_ramp': 29.92806488},
'q03': {'ec': 6.2550610547000005, 'ej': 25.1411401038, 'el': 5.000964059},
'q12': {'ec': 6.2233456874, 'ej': 25.1512697752, 'el': 5.6012793457},
'q12_pulse_rampcos': { 'amp': 0.17872194,
'omega_d': 4.19989714,
'phase': -0.01543561,
't_plateau': 69.95327862,
't_ramp': 29.94562879},
'q13': {'ec': 6.2867872105, 'ej': 25.1316550509, 'el': 6.9144529962},
'q22': { 'ec': 6.282846661600001,
'ej': 25.1329965509,
'el': 7.539286440900001},
'q22_pulse_rampcos': { 'amp': 0.23370657,
'omega_d': 3.65018191,
'phase': -0.50006247,
't_plateau': 69.96302405,
't_ramp': 29.98769304},
'single_q_compensation': { 'post_comp_q02': [ -0.01528741,
0.04418319,
-0.374539],
'post_comp_q03': [ -1.00629771,
0.16411027,
-0.00797514],
'post_comp_q12': [ 0.00200104,
0.58679953,
-1.74804011],
'post_comp_q13': [ -0.11690826,
1.09973631,
-1.0941268],
'post_comp_q22': [ -0.00142308406,
-1.59853235,
-0.188352743],
'post_comp_q23': [ 1.65404972,
-0.67251125,
-0.04376101],
'pre_comp_q02': [ 0.05671005,
0.02682845,
0.0395099],
'pre_comp_q03': [ -1.46070525,
0.66029355,
0.06603344],
'pre_comp_q12': [ 0.00185588,
1.00099539,
-0.22439037],
'pre_comp_q13': [ -1.02837338,
-1.10918103,
-0.80985523],
'pre_comp_q22': [ -0.000190040736,
-1.34752499,
0.315152817],
'pre_comp_q23': [ 0.36516325,
-0.38763126,
-0.41575102]}}
For optimization, let us use an object function based on the average gate fidelity with leakage. The formula is from Physical Review A 87, 022309 (2013).
\[C = 1 - \frac{1}{D(D + 1)}[Tr(PU^{\dagger}_{\text{sim}}P U_{\text{sim}}P)+|Tr(PU^{\dagger}_{\text{sim}} P U_{\text{target}})|^2],\]
where \(P\) is the projector into the computational subspace \(S\), and \(D=2^n\) is the dimension of \(S\).
[3]:
def infidelity(params, target_unitary):
params = hk.data_structures.merge(evo.all_params, params)
# Compute the time evolution unitary in the eigenbasis.
sim_u = evo.eigen_basis(params)
# calculate fidelity
fidelity_vz, _ = compute_fidelity_with_1q_rotation_axis(target_unitary,
sim_u,
opt_method=None)
return jnp.log10(1 - fidelity_vz)
[4]:
vg = jax.value_and_grad(infidelity)(params, target_unitary)
print('The infidelity of simultaneous X gates is ', 10**vg[0])
The infidelity of simultaneous X gates is 0.0006533133102443198
[5]:
print('The gradient of log10(infidelity):')
tree_print(vg[1])
The gradient of log10(infidelity):
{ 'capacitive_coupling_all_unify': {'strength': -0.6174425407884527},
'inductive_coupling_all_unify': {'strength': -173.96144719936575},
'q02': { 'ec': -0.6837125510091742,
'ej': 0.7890854528150837,
'el': -1.6290934230152256},
'q02_pulse_rampcos': { 'amp': -2.7062878919217415,
'omega_d': 9.05269806452722,
'phase': 0.17580455750765092,
't_plateau': 0.0004461406938806923,
't_ramp': 0.0021710917193119026},
'q03': { 'ec': -15.487466514428041,
'ej': 4.994560734982213,
'el': -14.771274450628626},
'q12': { 'ec': -6.135583830537494,
'ej': 2.390217074963847,
'el': -6.376757455310467},
'q12_pulse_rampcos': { 'amp': 0.366618193314487,
'omega_d': -5.987163304478852,
'phase': -0.032830858575709734,
't_plateau': -0.0038226864821826224,
't_ramp': -0.0071268422537319465},
'q13': { 'ec': 19.79925826507496,
'ej': -5.755877863966622,
'el': 15.949547405139802},
'q22': { 'ec': -2.837190082655775,
'ej': 1.1024237781374757,
'el': -2.6630469553039315},
'q22_pulse_rampcos': { 'amp': -0.8268418823571508,
'omega_d': 4.316807071999165,
'phase': 0.08195071032814188,
't_plateau': -0.09959495534501038,
't_ramp': -0.19923209709062512},
'single_q_compensation': { 'post_comp_q02': [ -0.043058708031541784,
-0.1081922882516082,
-0.00047245680780019203],
'post_comp_q03': [ -0.040132314689419246,
0.09286322177842976,
0.04777320715082106],
'post_comp_q12': [ 0.02076305745652751,
-0.11026160999313106,
-0.0007921329017956955],
'post_comp_q13': [ -0.052512984019358555,
0.13272249357839883,
-0.03547022047954318],
'post_comp_q22': [ 0.02566362912610809,
-0.04357480940549863,
0.00013854753004495849],
'post_comp_q23': [ -0.06842996508179766,
-0.08691519549779514,
-0.254996352237459],
'pre_comp_q02': [ 0.06123823019615981,
0.00609265642076906,
0.0002779052419779024],
'pre_comp_q03': [ -0.1424971182463537,
0.16254251394582298,
-0.39623275245163825],
'pre_comp_q12': [ 0.08362049067037125,
-0.11239431738101757,
-0.0013775454285768624],
'pre_comp_q13': [ 0.06384665005560919,
0.015537243289941216,
-0.03840213095782019],
'pre_comp_q22': [ 0.025868405208411493,
-0.04332764944211931,
0.000106439648963539],
'pre_comp_q23': [ 0.029448959058193225,
-0.016853262924030332,
0.06583402673998862]}}
Show the optimization procedure of control parameters
[6]:
adam_opt(infidelity,
params,
args=(target_unitary,),
options={
'adam_lr': 1e-6,
'steps': 3
})
{'adam_lr': 1e-06, 'adam_lr_decay_rate': 1000, 'steps': 3, 'adam_b1': 0.9, 'adam_b2': 0.999}
step: 0
parameters:
{ 'capacitive_coupling_all_unify': {'strength': 0.07225663},
'inductive_coupling_all_unify': {'strength': -0.01256637},
'q02': {'ec': 6.3532393823, 'ej': 25.1103087675, 'el': 6.344787656},
'q02_pulse_rampcos': { 'amp': 0.18128846,
'omega_d': 2.58934559,
'phase': -0.24290228,
't_plateau': 69.93608145,
't_ramp': 29.92806488},
'q03': {'ec': 6.2550610547000005, 'ej': 25.1411401038, 'el': 5.000964059},
'q12': {'ec': 6.2233456874, 'ej': 25.1512697752, 'el': 5.6012793457},
'q12_pulse_rampcos': { 'amp': 0.17872194,
'omega_d': 4.19989714,
'phase': -0.01543561,
't_plateau': 69.95327862,
't_ramp': 29.94562879},
'q13': {'ec': 6.2867872105, 'ej': 25.1316550509, 'el': 6.9144529962},
'q22': { 'ec': 6.282846661600001,
'ej': 25.1329965509,
'el': 7.539286440900001},
'q22_pulse_rampcos': { 'amp': 0.23370657,
'omega_d': 3.65018191,
'phase': -0.50006247,
't_plateau': 69.96302405,
't_ramp': 29.98769304},
'single_q_compensation': { 'post_comp_q02': [ -0.01528741,
0.04418319,
-0.374539],
'post_comp_q03': [ -1.00629771,
0.16411027,
-0.00797514],
'post_comp_q12': [ 0.00200104,
0.58679953,
-1.74804011],
'post_comp_q13': [ -0.11690826,
1.09973631,
-1.0941268],
'post_comp_q22': [ -0.00142308406,
-1.59853235,
-0.188352743],
'post_comp_q23': [ 1.65404972,
-0.67251125,
-0.04376101],
'pre_comp_q02': [ 0.05671005,
0.02682845,
0.0395099],
'pre_comp_q03': [ -1.46070525,
0.66029355,
0.06603344],
'pre_comp_q12': [ 0.00185588,
1.00099539,
-0.22439037],
'pre_comp_q13': [ -1.02837338,
-1.10918103,
-0.80985523],
'pre_comp_q22': [ -0.000190040736,
-1.34752499,
0.315152817],
'pre_comp_q23': [ 0.36516325,
-0.38763126,
-0.41575102]}}
gradient:
{ 'capacitive_coupling_all_unify': {'strength': -0.6174426358319549},
'inductive_coupling_all_unify': {'strength': -12.566370000000001},
'q02': { 'ec': -0.6836903614874095,
'ej': 0.7890782993504665,
'el': -1.6290739636354412},
'q02_pulse_rampcos': { 'amp': -2.706287594040715,
'omega_d': 9.052702379285883,
'phase': 0.17580453778257207,
't_plateau': 0.00044614275380611265,
't_ramp': 0.002171093927636296},
'q03': { 'ec': -15.48748399817091,
'ej': 4.994565964834739,
'el': -14.771290379371939},
'q12': { 'ec': -6.135621488562897,
'ej': 2.3902286561444765,
'el': -6.376791260776997},
'q12_pulse_rampcos': { 'amp': 0.36663851686395077,
'omega_d': -5.987162839452231,
'phase': -0.032830861715532234,
't_plateau': -0.0038226667160286346,
't_ramp': -0.00712682752429529},
'q13': { 'ec': 19.799243519751897,
'ej': -5.755873034999753,
'el': 15.949534653862633},
'q22': { 'ec': -2.8372140080147834,
'ej': 1.102431602270048,
'el': -2.6630669947471803},
'q22_pulse_rampcos': { 'amp': -0.8268529692505551,
'omega_d': 4.316802020857058,
'phase': 0.08195072989557077,
't_plateau': -0.09959707998861049,
't_ramp': -0.19923632766690863},
'single_q_compensation': { 'post_comp_q02': [ -0.043058708035501034,
-0.10819229188716406,
-0.0004724568073855888],
'post_comp_q03': [ -0.040132352646814695,
0.09286307122511062,
0.04777321222543768],
'post_comp_q12': [ 0.020763057368499926,
-0.11026230579466301,
-0.0007921329004860385],
'post_comp_q13': [ -0.052512993503057714,
0.13272230650318306,
-0.03547021861164853],
'post_comp_q22': [ 0.02566362912646314,
-0.043575145891900036,
0.00013854752964865167],
'post_comp_q23': [ -0.06842981337537424,
-0.08691522067783808,
-0.25499646979634616],
'pre_comp_q02': [ 0.06123823017260248,
0.006092652729442705,
0.00027790524247492106],
'pre_comp_q03': [ -0.14249725291475812,
0.16254248112011283,
-0.39623285451535095],
'pre_comp_q12': [ 0.08362049233989266,
-0.11239501317868417,
-0.0013775454279784582],
'pre_comp_q13': [ 0.06384671304766698,
0.015537147182044464,
-0.03840224215776128],
'pre_comp_q22': [ 0.02586840462457224,
-0.04332798592866993,
0.00010643964885003368],
'pre_comp_q23': [ 0.029448998450604336,
-0.01685297418538312,
0.0658340317744445]}}
loss: -3.184878493365988
step: 1
parameters:
{ 'capacitive_coupling_all_unify': {'strength': 0.07225762999998381},
'inductive_coupling_all_unify': {'strength': -0.012565370000000796},
'q02': { 'ec': 6.353240382299985,
'ej': 25.110307767500014,
'el': 6.344788655999994},
'q02_pulse_rampcos': { 'amp': 0.18128945999999632,
'omega_d': 2.5893445900000014,
'phase': -0.24290327999994313,
't_plateau': 69.93608045002242,
't_ramp': 29.928063880004608},
'q03': { 'ec': 6.2550620547,
'ej': 25.141139103800004,
'el': 5.000965058999999},
'q12': { 'ec': 6.2233466873999985,
'ej': 25.151268775200002,
'el': 5.601280345699998},
'q12_pulse_rampcos': { 'amp': 0.17872094000002728,
'omega_d': 4.199898139999998,
'phase': -0.015434610000304593,
't_plateau': 69.95327961999739,
't_ramp': 29.9456297899986},
'q13': {'ec': 6.286786210500001, 'ej': 25.1316560509, 'el': 6.9144519962},
'q22': { 'ec': 6.282847661599997,
'ej': 25.13299555090001,
'el': 7.539287440899997},
'q22_pulse_rampcos': { 'amp': 0.2337075699999879,
'omega_d': 3.6501809100000027,
'phase': -0.500063469999878,
't_plateau': 69.9630250499999,
't_ramp': 29.98769403999995},
'single_q_compensation': { 'post_comp_q02': [ -0.01528641000023224,
0.04418418999990757,
-0.3745380000211655],
'post_comp_q03': [ -1.0062967100002491,
0.1641092700001077,
-0.007976139999790677],
'post_comp_q12': [ 0.0020000400004816245,
0.5868005299999093,
-1.748039110012624],
'post_comp_q13': [ -0.11690726000019043,
1.0997353100000753,
-1.0941258000002818],
'post_comp_q22': [ -0.0014240840596103437,
-1.5985313500002294,
-0.1883537429278278],
'post_comp_q23': [ 1.6540507199998538,
-0.6725102500001151,
-0.04376001000003922],
'pre_comp_q02': [ 0.0567090500001633,
0.026827450001641318,
0.039508900035982196],
'pre_comp_q03': [ -1.4607042500000702,
0.6602925500000615,
0.06603443999997476],
'pre_comp_q12': [ 0.0018548800001195879,
1.000996389999911,
-0.22438937000725925],
'pre_comp_q13': [ -1.0283743799998433,
-1.1091820299993564,
-0.8098542300002605],
'pre_comp_q22': [ -0.00019104073561342818,
-1.3475239900002307,
0.3151518170939411],
'pre_comp_q23': [ 0.3651622500003396,
-0.38763026000059336,
-0.4157520199998481]}}
gradient:
{ 'capacitive_coupling_all_unify': {'strength': -0.1080880595446197},
'inductive_coupling_all_unify': {'strength': -12.565370000000796},
'q02': { 'ec': 20.141591910072126,
'ej': -5.930759419524341,
'el': 16.675921583204655},
'q02_pulse_rampcos': { 'amp': 1.0761685942197574,
'omega_d': 5.099704378723308,
'phase': 0.14075338627895886,
't_plateau': 0.0028335812079036656,
't_ramp': 0.002984928999600904},
'q03': { 'ec': -6.242799690165127,
'ej': 2.227837636202891,
'el': -6.343089503362728},
'q12': { 'ec': 10.05510014481463,
'ej': -2.5937343106779926,
'el': 8.176451400339001},
'q12_pulse_rampcos': { 'amp': -9.230360358083175,
'omega_d': -0.5659509978100496,
'phase': -0.0008000716460606766,
't_plateau': -0.013991353699103037,
't_ramp': -0.015172977010096171},
'q13': { 'ec': 4.032120621754349,
'ej': -0.5981725607150838,
'el': 2.3349775883486035},
'q22': { 'ec': 11.714975942036652,
'ej': -3.6618788087293566,
'el': 9.541669499530972},
'q22_pulse_rampcos': { 'amp': 5.471272935011095,
'omega_d': -0.11345921790467894,
'phase': 0.04601041237921856,
't_plateau': 1.1622248985027355,
't_ramp': 2.3148122433194933},
'single_q_compensation': { 'post_comp_q02': [ -0.04296440351322228,
0.007735798866306709,
-0.000472442049755578],
'post_comp_q03': [ -0.023805494076306388,
0.1546453560590919,
0.04579675308674034],
'post_comp_q12': [ 0.020618619701184282,
0.19124127707323169,
-0.0007922890058023417],
'post_comp_q13': [ -0.06043942920420875,
-0.03654442274478242,
-0.03389347005695273],
'post_comp_q22': [ 0.02456974397988998,
0.15953921147453798,
0.00013789954057001852],
'post_comp_q23': [ 0.0065777070487484934,
-0.09841037977719225,
-0.3108613492216434],
'pre_comp_q02': [ 0.06106671979981657,
0.12186724948907823,
0.0002776366569465278],
'pre_comp_q03': [ -0.0447067748920702,
0.18159589448021052,
-0.3228202126889376],
'pre_comp_q12': [ 0.08238892689791699,
0.18910794838120637,
-0.0013768746932801056],
'pre_comp_q13': [ 0.15161574735952854,
-0.12007866290616676,
-0.193624035606686],
'pre_comp_q22': [ 0.02443383024925505,
0.15978598567403424,
0.00010700296342389427],
'pre_comp_q23': [ 0.05799420227852181,
0.19963372948831193,
0.06945727661840752]}}
loss: -3.184968089352475
step: 2
parameters:
{ 'capacitive_coupling_all_unify': {'strength': 0.07225841784304814},
'inductive_coupling_all_unify': {'strength': -0.012564370694982277},
'q02': { 'ec': 6.353239661810791,
'ej': 25.110308416365157,
'el': 6.344787980969019},
'q02_pulse_rampcos': { 'amp': 0.1812898072665362,
'omega_d': 2.5893436415587234,
'phase': -0.24290426750264416,
't_plateau': 69.93607961135304,
't_ramp': 29.9280628847983},
'q03': { 'ec': 6.255062953823664,
'ej': 25.141138189282422,
'el': 5.000965967757341},
'q12': { 'ec': 6.223346401191563,
'ej': 25.15126886851925,
'el': 5.601280170872535},
'q12_pulse_rampcos': { 'amp': 0.1787216564732163,
'omega_d': 4.199898876634688,
'phase': -0.01543392248021462,
't_plateau': 69.95328051374486,
't_ramp': 29.945630747686078},
'q13': { 'ec': 6.286785405922782,
'ej': 25.131656793807636,
'el': 6.914451225906028},
'q22': { 'ec': 6.282847096388397,
'ej': 25.132996070043063,
'el': 7.539286904546196},
'q22_pulse_rampcos': { 'amp': 0.23370693472780815,
'omega_d': 3.6501802601851705,
'phase': -0.5000644179805752,
't_plateau': 69.96302436623456,
't_ramp': 29.987693356506497},
'single_q_compensation': { 'post_comp_q02': [ -0.015285410751082446,
0.04418480482676854,
-0.3745370007360374],
'post_comp_q03': [ -1.0062957546608526,
0.1641082878202255,
-0.007977137983646118],
'post_comp_q12': [ 0.00199904088178976,
0.5868002200327866,
-1.7480381107130172],
'post_comp_q13': [ -0.11690625949792513,
1.0997348619514866,
-1.0941248021354821],
'post_comp_q22': [ -0.001425081995279016,
-1.5985318910277204,
-0.18835474203778305],
'post_comp_q23': [ 1.65405131533176,
-0.6725092493848097,
-0.04375901040920896],
'pre_comp_q02': [ 0.05670805076727027,
0.026826673889808656,
0.03950790079016599],
'pre_comp_q03': [ -1.460703388435343,
0.6602915493412499,
0.06603542884718137],
'pre_comp_q12': [ 0.0018538811071118987,
1.0009960926563752,
-0.2243883707201376],
'pre_comp_q13': [ -1.028375325144863,
-1.1091813784011246,
-0.8098533703699619],
'pre_comp_q22': [ -0.00019203815103289822,
-1.347524532569066,
0.31515081774648046],
'pre_comp_q23': [ 0.36516128385008656,
-0.3876309446902434,
-0.4157530203432777]}}
gradient:
{ 'capacitive_coupling_all_unify': {'strength': 0.2547241566218455},
'inductive_coupling_all_unify': {'strength': -8.848418763687231},
'q02': { 'ec': 7.7773179061752975,
'ej': -1.9667158244224672,
'el': 5.874042597049834},
'q02_pulse_rampcos': { 'amp': -7.509203303696441,
'omega_d': 0.3269734576612713,
'phase': 0.10299770579273182,
't_plateau': -0.00720523623387889,
't_ramp': -0.005574423005673205},
'q03': { 'ec': 2.279034181411048,
'ej': -0.32452132788002863,
'el': 1.43426791125959},
'q12': { 'ec': 11.443287287557515,
'ej': -3.027399226446643,
'el': 9.448966454189403},
'q12_pulse_rampcos': { 'amp': -10.576133518871245,
'omega_d': 2.7689416718759507,
'phase': 0.014141603880629974,
't_plateau': -0.015912133833155442,
't_ramp': -0.016947796375671904},
'q13': { 'ec': -10.416029199427479,
'ej': 4.131278623788382,
'el': -10.151340045531787},
'q22': { 'ec': -2.371875454580172,
'ej': 0.9432988021747202,
'el': -2.2525668247051596},
'q22_pulse_rampcos': { 'amp': -1.1919578460501752,
'omega_d': -2.382675560566544,
'phase': 0.007907204375479968,
't_plateau': 0.047497212700466404,
't_ramp': 0.0962327158131592},
'single_q_compensation': { 'post_comp_q02': [ -0.043091615359367744,
-0.2109559622284607,
-0.00047244024378078394],
'post_comp_q03': [ -0.00929057624384555,
0.20932666888856488,
0.044068986531278435],
'post_comp_q12': [ 0.020387897373924768,
0.22035496684418376,
-0.0007919645283211825],
'post_comp_q13': [ -0.06917519951974782,
-0.20385353076346746,
-0.03229034731147464],
'post_comp_q22': [ 0.023133383328972634,
-0.038857441791748215,
0.00013819880793177033],
'post_comp_q23': [ -0.0004954051497918499,
-0.0972165415452082,
-0.30323461318606965],
'pre_comp_q02': [ 0.06139265648519339,
-0.09650336922432246,
0.00027714490619904777],
'pre_comp_q03': [ 0.048606301370874405,
0.1995209685504733,
-0.25276159499854256],
'pre_comp_q12': [ 0.08175297655164537,
0.2182225307161616,
-0.0013762150610613678],
'pre_comp_q13': [ 0.2266281786972968,
-0.23432770851024998,
-0.3265645081251528],
'pre_comp_q22': [ 0.02322060302733156,
-0.03860974489987257,
0.00010753224365646595],
'pre_comp_q23': [ 0.06322475773308156,
0.24137043668287106,
0.07003749721193685]}}
loss: -3.1850026625115544
[6]:
{'capacitive_coupling_all_unify': {'strength': Array(0.07225874, dtype=float64)},
'inductive_coupling_all_unify': {'strength': Array(-0.0125634, dtype=float64)},
'q02': {'ec': Array(6.35323891, dtype=float64),
'ej': Array(25.11030909, dtype=float64),
'el': Array(6.34478728, dtype=float64)},
'q02_pulse_rampcos': {'amp': Array(0.1812905, dtype=float64),
'omega_d': Array(2.58934289, dtype=float64),
'phase': Array(-0.24290523, dtype=float64),
't_plateau': Array(69.93607996, dtype=float64),
't_ramp': Array(29.92806299, dtype=float64)},
'q03': {'ec': Array(6.25506356, dtype=float64),
'ej': Array(25.14113752, dtype=float64),
'el': Array(5.00096661, dtype=float64)},
'q12': {'ec': Array(6.2233458, dtype=float64),
'ej': Array(25.15126934, dtype=float64),
'el': Array(5.60127964, dtype=float64)},
'q12_pulse_rampcos': {'amp': Array(0.1787225, dtype=float64),
'omega_d': Array(4.19989913, dtype=float64),
'phase': Array(-0.01543369, dtype=float64),
't_plateau': Array(69.95328145, dtype=float64),
't_ramp': Array(29.94563172, dtype=float64)},
'q13': {'ec': Array(6.28678515, dtype=float64),
'ej': Array(25.13165689, dtype=float64),
'el': Array(6.91445106, dtype=float64)},
'q22': {'ec': Array(6.28284679, dtype=float64),
'ej': Array(25.13299631, dtype=float64),
'el': Array(7.53928664, dtype=float64)},
'q22_pulse_rampcos': {'amp': Array(0.23370659, dtype=float64),
'omega_d': Array(3.65018013, dtype=float64),
'phase': Array(-0.5000652, dtype=float64),
't_plateau': Array(69.96302381, dtype=float64),
't_ramp': Array(29.9876928, dtype=float64)},
'single_q_compensation': {'post_comp_q02': Array([-0.01528441, 0.04418559, -0.374536 ], dtype=float64),
'post_comp_q03': Array([-1.00629491, 0.16410731, -0.00797813], dtype=float64),
'post_comp_q12': Array([ 0.00199804, 0.5867996 , -1.74803711], dtype=float64),
'post_comp_q13': Array([-0.11690526, 1.0997352 , -1.09412381], dtype=float64),
'post_comp_q22': Array([-1.42607612e-03, -1.59853215e+00, -1.88355740e-01], dtype=float64),
'post_comp_q23': Array([ 1.65405178, -0.67250825, -0.04375801], dtype=float64),
'pre_comp_q02': Array([0.05670705, 0.0268266 , 0.0395069 ], dtype=float64),
'pre_comp_q03': Array([-1.46070295, 0.66029055, 0.0660364 ], dtype=float64),
'pre_comp_q12': Array([ 0.00185288, 1.00099548, -0.22438737], dtype=float64),
'pre_comp_q13': Array([-1.02837627, -1.10918058, -0.80985248], dtype=float64),
'pre_comp_q22': Array([-1.93032056e-04, -1.34752480e+00, 3.15149819e-01], dtype=float64),
'pre_comp_q23': Array([ 0.3651603 , -0.38763177, -0.41575402], dtype=float64)}}