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

Ramakrishna Pasupuleti

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.7133301

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

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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.

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Scalable quantum architectures are increasingly constrained by mesoscopic structural effects that emerge prior to hardware realization. While a foundational admissibility layer establishes spectral consistency of scaling families, admissibility alone doesnot ensure favorable physical scaling behavior as architectures grow. This work introduces a second diagnostic layer aimed at quantifying architecture-level structural risk under controlled growth. Assuming admissibility has been verified, we analyze parametrized architectural graph families and extract a minimal set of physically interpretable observables derived from a symmetric structural operator. By examining the scaling of load concentration, mediation overhead, and perturbationinduced amplification, we estimate global and local growth exponents and define a Relative Structural Risk Index (SRI) for comparative assessment across architectures. The framework is evaluated on multiple architectural families, including linear chains, planar grids, centralized star topologies, modular hierarchical constructions, and a constraintmediated architecture inspired by parity-based encodings. The results indicate that spectrally admissible architectures can nonetheless exhibit substantially different mesoscopic scaling regimes. The proposed diagnostic layer functions as a pre-hardware structural assessment instrument, supporting architecture-level decisions prior to layout optimization, hardware prototyping, or error-correction design.

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Test Systems for Quantum Information Processing

Ion I. Geru, Dieter Suter

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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.
Scalable quantum architectures are increasingly constrained by mesoscopic structural effects that emerge prior to hardware realization. While a foundational admissibility layer establishes spectral consistency of scaling families, admissibility alone doesnot ensure favorable physical scaling behavior as architectures grow. This work introduces a second diagnostic layer aimed at quantifying architecture-level structural risk under controlled growth. Assuming admissibility has been verified, we analyze parametrized architectural graph families and extract a minimal set of physically interpretable observables derived from a symmetric structural operator. By examining the scaling of load concentration, mediation overhead, and perturbationinduced amplification, we estimate global and local growth exponents and define a Relative Structural Risk Index (SRI) for comparative assessment across architectures. The framework is evaluated on multiple architectural families, including linear chains, planar grids, centralized star topologies, modular hierarchical constructions, and a constraintmediated architecture inspired by parity-based encodings. The results indicate that spectrally admissible architectures can nonetheless exhibit substantially different mesoscopic scaling regimes. The proposed diagnostic layer functions as a pre-hardware structural assessment instrument, supporting architecture-level decisions prior to layout optimization, hardware prototyping, or error-correction design.
This technical note investigates whether Layer II structural diagnostics can predict transpilation costs in IBM Heavy-Hex quantum architectures before compilation is performed. The study evaluates Heavy-Hex coupling maps generated through Qiskit for distances d = 3, 5, 7, and 9. Structural observables are computed directly from the architecture graph and compared against normalized transpilation metrics obtained from Quantum Fourier Transform (QFT), GHZ-state preparation, and Hardware-Efficient Ansatz (HEA) circuits. A leave-one-out validation procedure is used to assess predictive capability on unseen architecture instances. Results indicate that mediation, normalized structural cost, and feasibility observables contain measurable predictive information regarding transpilation overhead. The strongest performance is observed for HEA and GHZ workloads, where several prediction targets achieve low error levels. The benchmark provides preliminary evidence that circuit-independent structural observables may support predictive assessment of compilation costs during early-stage quantum architecture evaluation.