Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Circuit-Level vs Pulse-Level Quantum Hardware Diagnostics: K-R Scaling Exponent Detects Device Degradation Invisible to T₁/T₂ Calibration

Ramakrishna Pasupuleti

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.7133299

Related papers

Circuit-Level vs Pulse-Level Quantum Hardware Diagnostics: K–R Scaling Exponent Detects Device Degradation Invisible to T₁/T₂ Calibration

Ramakrishna Pasupuleti

Abstract Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 µs to 340 µs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n = 2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 µs, compared to IBM-reported pulse-level T₂ of 32–340 µs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜ v ₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.

•Jul 22, 2026•0 citations

Pulse-level characterization of qubits on quantum devices

Quiroga Salamanca, David Balaguer Andrés

•Jan 1, 2021•1 citation

Designing Quantum Controls Efficiently

Alicia B Magann

a) Pulse level (b) Circuit level

•Jan 1, 2021•0 citations

A Low-Cost Diagnostic Framework for NISQ Noise Regimes Using the K–R Scaling Exponent on IBM Quantum Hardware

Ramakrishna Rao Pasupuleti

Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 μs to 340 μs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n=2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 μs, compared to IBM-reported pulse-level T₂ of 32–340 μs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜᵥₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.

•Apr 17, 2026•0 citations

Toward a Quantum Diagnostic Intelligence System: A Multi-Layer Architecture for Circuit-Level Assessment of Quantum Hardware Operational State

Ramakrishna Pasupuleti

Abstract We introduce Quantum Diagnostic Intelligence (QDI), a five-layer decision-support architecture for inferring the operational state of quantum processing units from circuit-level measurements. Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the dominant industry standard for certifying quantum hardware health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors, that circuit-level measurements contain diagnostic information that T₁/T₂ may not capture. On ibm_marrakesh between July 3 and July 5, 2026, IBM-reported T₂ improved from 303 to 340 µs (+ 12%), while circuit-level GHZ scaling yielded α = 3.811, a QDI health score of 37.8/100 [95% CI: 7.6, 67.9], and a circuit infidelity increase of 920% at n = 2 qubits. The QDI architecture formalizes this observation through four operators: a diagnostic state classifier D(χ), a health score H(χ), a recommendation generator R(χ), and a drift forecast F(χ, t). Rules trained on ibm_kingston correctly classified ibm_fez and both ibm_marrakesh sessions without retuning (3/3 held-out tests; n = 4 total — feasibility demonstration only). We identify three scientific limitations of the current framework: operators rely on expert-designed rules rather than learned statistical models; ground truth (algorithm success/failure) has not yet been collected; and diagnostic state labels have not been validated by clustering analysis. Prospective validation on n ≥ 50 independent sessions is required before statistical claims about generalization can be made. The dataset, code, and QDI system are openly available.

•Jul 22, 2026•0 citations

What does a continuously monitored qubit readout really show

Justin Dressel, Luis Pedro García-Pintos

Bulletin of the American Physical Society•Mar 16, 2017•0 citations
Abstract Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 µs to 340 µs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n = 2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 µs, compared to IBM-reported pulse-level T₂ of 32–340 µs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜ v ₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.
Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the standard by which quantum hardware providers certify device health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors (ibm_kingston, ibm_fez, and ibm_marrakesh), that these pulse-level metrics can give opposite conclusions to circuit-level diagnostics on the same device at the same time. Specifically, between experimental sessions on July 3 and July 5, 2026, IBM reported ibm_marrakesh T₂ improved from 303 μs to 340 μs (a 12% improvement), while the K–R scaling exponent α measured from GHZ circuit scaling increased from 1.406 to 3.811 (a 171% increase in coherent error dominance) and GHZ n=2 infidelity rose from 0.018 to 0.204 — a 920% increase in circuit error. We further demonstrate that Ramsey circuit-context T₂ on qubit 0 is approximately 9–30 μs, compared to IBM-reported pulse-level T₂ of 32–340 μs — a discrepancy of 3–11×. We introduce five experimental demonstrations showing K–R circuit-level diagnostics detect degradation invisible to T₁/T₂, provide higher temporal sensitivity, enable algorithm failure prediction, and confirm the theoretical prediction αₜᵥₜᶜʰ + αₑₜₕₒ = 2 within 7% on real hardware. These findings establish circuit-level K–R diagnostics as a necessary complement to pulse-level calibration for operational quantum computing.
Abstract We introduce Quantum Diagnostic Intelligence (QDI), a five-layer decision-support architecture for inferring the operational state of quantum processing units from circuit-level measurements. Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the dominant industry standard for certifying quantum hardware health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors, that circuit-level measurements contain diagnostic information that T₁/T₂ may not capture. On ibm_marrakesh between July 3 and July 5, 2026, IBM-reported T₂ improved from 303 to 340 µs (+ 12%), while circuit-level GHZ scaling yielded α = 3.811, a QDI health score of 37.8/100 [95% CI: 7.6, 67.9], and a circuit infidelity increase of 920% at n = 2 qubits. The QDI architecture formalizes this observation through four operators: a diagnostic state classifier D(χ), a health score H(χ), a recommendation generator R(χ), and a drift forecast F(χ, t). Rules trained on ibm_kingston correctly classified ibm_fez and both ibm_marrakesh sessions without retuning (3/3 held-out tests; n = 4 total — feasibility demonstration only). We identify three scientific limitations of the current framework: operators rely on expert-designed rules rather than learned statistical models; ground truth (algorithm success/failure) has not yet been collected; and diagnostic state labels have not been validated by clustering analysis. Prospective validation on n ≥ 50 independent sessions is required before statistical claims about generalization can be made. The dataset, code, and QDI system are openly available.