Skip to content
aiqbench takeQuantum Computing

aiqbench analysis

This arXiv preprint tests whether zero-noise extrapolation, a common quantum error-mitigation technique, can be trusted when noise sources are amplified separately. Using a pre-registered factorial experiment on three IBM dynamic circuits, the authors find that a weak interaction between knobs does not guarantee an accurate zero-noise limit, and that several strategies still exclude the known ideal value. For readers building or evaluating error mitigation, the main takeaway is that the work is a diagnostic of the response model, not evidence that the mitigation is accurate.
Sources coveringarXiv, METR, Latent Space

Separability does not certify zero-noise extrapolation: Factorial diagnostics in dynamic quantum circuits

arXiv3 min read

arXiv:2610.28705v1 Announce Type: new Abstract: Separately amplifying gate, latency, and mid-circuit measurement errors can simplify zero-noise extrapolation, but a weak interaction between amplification knobs does not establish an accurate zero-noise limit. We distinguish observable separability, response-model adequacy, and calibration of that limit, and test them in a pre-specified $2^3$ factorial experiment on three IBM dynamic circuits. For the primary echoed spectator observable, the latency-measurement coefficient is $b_{LM}=+0.0007$ with a nominal 95% bootstrap interval $[-0.0033,+0.0049]$. A separately planned follow-up meets a $\pm0.005$ equivalence criterion and recovers two injected interactions; both repeat runs nevertheless trigger the drift rule. Multi-knob extrapolations have smaller median absolute errors than the single-knob comparisons, yet all five original strategies exclude the known ideal value for three of six observables. Duration controls support a model-dependent contribution to the spectator deficit from unequal decay rates in native padding and added delay. We give an explicit construction showing why measurements at two measurement-knob levels cannot identify the extrapolated limit without response assumptions. A post-hoc precision analysis and an additive full-factorial estimator expose a further bias-variance trade-off hidden by point-error comparisons. The experiment provides a diagnostic of the implemented response surface, not a certification of physical channel independence or mitigation accuracy.

Abstract:Separately amplifying gate, latency, and mid-circuit measurement errors can simplify zero-noise extrapolation, but a weak interaction between amplification knobs does not establish an accurate zero-noise limit. We distinguish observable separability, response-model adequacy, and calibration of that limit, and test them in a pre-specified $2^3$ factorial experiment on three IBM dynamic circuits. For the primary echoed spectator observable, the latency-measurement coefficient is $b_{LM}=+0.0007$ with a nominal 95% bootstrap interval $[-0.0033,+0.0049]$. A separately planned follow-up meets a $\pm0.005$ equivalence criterion and recovers two injected interactions; both repeat runs nevertheless trigger the drift rule. Multi-knob extrapolations have smaller median absolute errors than the single-knob comparisons, yet all five original strategies exclude the known ideal value for three of six observables. Duration controls support a model-dependent contribution to the spectator deficit from unequal decay rates in native padding and added delay. We give an explicit construction showing why measurements at two measurement-knob levels cannot identify the extrapolated limit without response assumptions. A post-hoc precision analysis and an additive full-factorial estimator expose a further bias-variance trade-off hidden by point-error comparisons. The experiment provides a diagnostic of the implemented response surface, not a certification of physical channel independence or mitigation accuracy.

arXiv:2610.28705 [quant-ph]

(or arXiv:2610.28705v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.28705 Focus to learn more arXiv-issued DOI via DataCite

LICENSE-code.txt

LICENSE-data.txt

analysis_report.json

analysis_report_h4.json

job1_manifest.json

job1_results.json

job1b_check.json

job1b_echo_pilot.py

job1b_manifest.json

job1b_results.json

job1c_check.json

job1c_manifest.json

job1c_results.json

job2_manifest.json

job2_results.json

job3_manifest.json

job3_results.json

job4_manifest.json

job4_results.json

job5_analysis.py

job5_manifest.json

job5_report.json

job5_results.json

plots/figure_sources/original_plots.py

plots/figure_sources/render_plots.py

post_hoc/analysis_report_twirl_s11.json

post_hoc/analysis_report_twirl_s7.json

post_hoc/job5_followup.json

post_hoc/job5_followup.py

post_hoc/rerun_twirl_bootstrap.py

post_hoc/tauff_results.json

post_hoc/tauff_sensitivity.py

post_hoc/window_checks.json

post_hoc/window_checks.py

precision/compute_precision.py

precision/precision_results.json

sim_validation.py

sim_validation_results.json

sim_validation_results_noecho.json

(53 additional files not shown) You must enabled JavaScript to view entire file list.

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 337 words

Related coverage