QLRO ▸Awaiting first observation — install qlro and run qlbraket.enable() to autolog.

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Vendor-neutral quantum-device feed — real-time queue and calibration health, your prediction track record, and where to run today.

snapshot 89cd842f · 21d agoqlro v0.14.1recommend a device →
no data0 devices · vendor-health worker polling every 30s
Top measuredreal measurement
ibm_kingston
F obs0.982
Δ vs pred+0.068
Last6d ago

highest observed fidelity in last 7d (n=3)

Q-MARKETstale0sago
Outcomes 24h060 total
Devices6/22measured / tracked
Model r0.869pred vs obs
Cron4d agolast reference run
Get this in codesame answer, your CI
import qlro

result = qlro.recommend(my_circuit, category="chemistry")
print(result.primary)   # → 'ibm_kingston'
pip install qlro · 0 configor auto-log every run: qlro.autolog.braket.enable()Full quickstart →
Where to run today · best per cost lane
14 devices · 6 on tier-frontier
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Same data plotted below. Bottom-right of the chart = highest quality + fastest wait. Solid dot = measured outcome, hollow = modelled, dashed ring= thin sample (n < 3).

0.50.60.70.80.91.0Result quality (F) — higher = cleaner output →1s10s1m10m1.0h1.0d↓ time to result — faster is better (log)ibm_kingstonionq_aria_1iqm_garnetrigetti_ankaa-3
Free<$1/run$1–5/run$5+/run·observedmodelledtier frontier

Daily Signals

live · 6 cards
Queue wait time
no data

Vendor-health worker has not posted yet. Cards light up after the first cron pass populates device_health.

Community activity
0 / 24h · 8 / 7d
By vendor · last 7d
  • IBM (8)
Your drift, by device
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Sign in to see which of your target devices' calibrations have shifted since you last ran. autolog records each (predicted, observed) pair so a widening gap surfaces here automatically.

Your r(τ) trend
detail →

Sign in to track how well your predictions match observed fidelity over time. Updates as autolog posts your runs.

Device Matrix · 22

DeviceProviderqΦFTΓN$/runqueue30d residuallast
iqm_garnetestaws200.260.990.510.5813~$1.75~5 sec15d
iqm_emeraldestaws540.260.980.510.519~$1.75~9 sec15d
wukong_102estorigin1020.010.960.510.018UnknownUnknown
wukong_72estorigin720.010.950.510.1023UnknownUnknown
H2-2estquantinuum560.750.940.510.8832$125K+/moN/A
ibm_bostonibm1560.420.820.510.8261Free~2 min
atom_computing_phoenixSpAtom Computing11800.260.800.510.690ContractUnknown
ionq_aria_1SpIonQ250.260.800.510.690~$30~30 sec
ionq_aria_2SpIonQ250.260.800.510.690~$30~30 sec
ionq_forte_awsSpIonQ360.260.800.510.690~$40~30 sec
ionq_forte_enterpriseSpIonQ400.260.800.510.690ContractUnknown
pasqal_fresnelSpPasqal1000.260.800.510.690ContractUnknown
quantinuum_h1_1SpQuantinuum200.260.800.510.690ContractUnknown
quera_aquilaSpQuEra2560.260.800.510.690~$1.75~12 sec
rigetti_ankaa-3estaws820.260.800.510.071~$0.65~10 sec
xanadu_borealisSpXanadu2160.260.800.510.690ContractUnknown
ibm_pittsburghibm1560.280.770.510.7650Free~2 min
ibm_torinoibm1330.150.760.500.6653Free~2 min
ibm_fezibm1560.190.970.520.7248Free~2 min6d
ibm_kingstonibm1560.290.980.520.7645Free~2 min6d
ibm_marrakeshibm1560.260.98n=20.510.7545Free~2 min6d
ibm_brisbaneestibm1270.260.340.510.693Free~2 min
Σ outcomes 60model r 0.869RMSE 0.169snapshot v1.1-companion-mintaccuracy detail →