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Mesoscopic Structural Risk Diagnostics for Quantum Architectures: Layer II

Pedro Pelegrini

Published Mar 4, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.5281/zenodo.18868497

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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.
Scalability remains a central challenge in large-scale quantum architectures. While device-level characterization is essential, many scalability constraints originate at the architectural level, where global coordination limits and asymptotic instabilities emerge prior to layout, compilation, or hardware modeling. We introduce a structural admissibility framework positioned as a pre-hardware diagnostic layer for architecture-level scalability assessment. Architectures are modeled as controlled scaling families, and admissibility is determined by a set of structural functionals governing growth consistency, compositional refinement, and perturbative stability. These functionalsestablish whether a family supports coherent large-scale expansion before physical realization. To obtain measurable diagnostics, each structural instance is associated with a symmetric operator from a minimally specified axiomatic class. The smallest non-zero eigenvalue defines a spectral gap observable whose scaling yields a coherence-like indicator, an effective structural dimension estimate, and a quantitative measure of scaling fragility. Baseline experiments using the normalized graph Laplacian show that purely structural observables already separate distinct scaling regimes—such as mediation-dominated 1D chains and dimension-supported 2D grids—without invoking device-level noise models.
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.
Efforts to scale quantum hardware are commonly framed as a challenge of suppressing local decoherence and stochastic noise through improved physical qubits and quantum error correction. Yet large-scale devices routinely exhibit performance degradation inconsistent with bounded-complexity local noise models, including nonstationary drift, bursty correlated events, and long-range crosstalk. We propose a constraint-based scaling framework in which the dominant limitation to scalability is not local error amplitude but the accumulation of constraint geometry: structured, correlated degrees of freedom that grow with system size and cannot be absorbed by standard endogenous noise models. We formalize this via (i) a bounded-complexity endogenous model class, (ii) a structured residual observable isolating unexplained degradation, and (iii) a coherence budget comparing effective control capacity to constraint burden. We identify a regime transition-the coherence wall-at which constraint burden outpaces control capacity, leading to rapid performance collapse despite stable local fidelities. We provide operational measurement protocols for extracting structured residual scaling, correlated dimensionality, and perturbation sensitivity from standard benchmarking data. The framework yields falsifiable architectural predictions and offers a diagnostic layer bridging quantum theory and scalable quantum engineering.
This paper develops an engineering-facing diagnostic bridge between the recoverability-based theory of structural irreversibility and practical evaluation in quantum error correction and monitored quantum platforms. The central problem addressed in this work is that structural recoverability, threshold-like margins, false-collapse, and Recoverability Index–type diagnostics provide useful theoretical distinctions, but they do not by themselves specify how experimentalists or engineers should organize platform-level data, compare regimes, or report recoverability-relevant behavior. The paper therefore introduces a proxy map from structural quantities to engineering diagnostics. Generalized recoverability is associated with recovery performance, decoder-assisted restoration, logical-state fidelity, and related reconstruction-sensitive quantities. Threshold-like margins are associated with entropy-like, noise-sensitive, or transition-localizing proxies. False-collapse is associated with mismatch windows in which collapse-like or transition-like indicators become strong while recovery-sensitive records still indicate residual recoverability. The paper also proposes a minimal diagnostic architecture for QEC and monitored platforms. This architecture consists of four layers: an appearance-sensitive proxy, a recoverability-sensitive proxy, a threshold-like locator, and a window-based reporting layer. Together, these layers support regime classification, early-warning detection, false-collapse-like mismatch identification, finite-window reporting, and cross-regime comparison. A QEC-oriented realization is presented to show how syndrome histories, logical-failure markers, logical recovery performance, entropy-like proxies, false-collapse window frequencies, and RI-like summaries can be organized into a coherent diagnostic workflow. The goal is not to replace detailed decoder analysis or hardware-specific benchmarking, but to provide a bridge-level language for deciding what should be logged, estimated, compared, and reported when studying the approach to effective irreversibility in error-managed quantum systems. This work should be read as an engineering and experimental companion to the broader recoverability program. Its contribution is to make structural irreversibility operationally legible by mapping theoretical quantities to measurable, computable, and reportable diagnostics in contemporary quantum platforms.