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

TaiDN-Q Adaptive Noise-Aware Quantum Experiment Runtime: Mathematical Framework for Stability, Sensitivity, Robustness, and Adaptive Search

DUC TAI NGUYEN

Published Sep 28, 2026• 0 citations
Publisher

Abstract

TaiDN-Q is a proposed adaptive runtime framework for quantum experiments in which hardware noise and runtime variation are treated as observable environmental conditions rather than as quantities that must simply be eliminated. The framework separates execution quality from local sensitivity and robustness. A runtime state is represented by Rk = (Xk, Ck, Bk, ηk, Mk, Sk), where Xk denotes the information or problem state, Ck the circuit configuration, Bk the backend state, ηk the noise and environmental state, Mk the learned model, and Sk the measured stability or performance state. A stability score combines fidelity, total variation distance, noise risk, and execution cost. Local noise sensitivity is estimated through finite differences, and the Noise Sensitivity Index (NSI) provides a normalized inverse-sensitivity measure. Robustness is then defined by penalizing local fragility, leading to a stable operating region rather than a single optimum. Finally, an adaptive runtime selects subsequent experiments using a surrogate model and an acquisition function that balances predicted quality, uncertainty, and sensitivity. The central experimentally testable question is whether adaptive search can identify robust quantum execution regimes with fewer quantum experiments than non-adaptive baselines under matched experimental budgets.

DOI
10.5281/zenodo.23014698

Related papers

TaiDN-Q: A Noise-Aware Quantum Experiment Runtime for Adaptive Structured Quantum Execution

NGUYEN DUC TAI

We introduce TaiDN-Q, a noise-aware quantum experiment runtime designed for adaptive structured quantum execution on NISQ-era quantum hardware. The framework integrates Information Dynamical Algebra (IDA), tensor-based structured representations, graph parameterized Hamiltonian compilation, adaptive noise modeling, backend independent execution, and reproducible experimental orchestration into a unified scientific runtime architecture. Rather than proposing a universal physical theory, TaiDN-Q is formulated as an experimentally executable infrastructure for transforming structured mathematical systems into adaptive quantum experiments under realistic noisy hardware conditions. The framework defines: • a structured information-dynamical representation layer, • a tensor-to-graph compilation pipeline, • Hamiltonian-based quantum execution, • adaptive noise-aware runtime evolution, • and reproducible benchmark-oriented experiment orchestration. TaiDN-Q extends the Quantum Gateways (QGR) execution architecture toward adaptive runtime-oriented quantum experimentation on IBM Quantum and simulator backends. The framework does not claim quantum supremacy, fault tolerance, emergent spacetime, or universal quantum advantage. Instead, it focuses on executable, observable, adaptive, and reproducible scientific quantum experimentation.

•May 28, 2026•0 citations

Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing

Halil Özbil

Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing proposes a structure-sensitive runtime architecture for adaptive execution in Noisy Intermediate-Scale Quantum (NISQ) computing. Rather than applying a fixed error mitigation strategy to every quantum circuit, the proposed framework introduces a pre-execution decision layer that analyzes circuit structure and estimates execution reliability before selecting an appropriate mitigation strategy. The paper defines the core concepts of Structure Profile, Structure Fingerprint, Structure Complexity Index (SCI), Runtime Risk, and Confidence Score, and integrates them into a layered runtime architecture consisting of Structure Analysis, Hardware Abstraction, Runtime Intelligence, Adaptive Optimization, and Knowledge layers. This work is intentionally presented as a position and methodology paper. It introduces the mathematical framework, architectural design, theoretical complexity analysis, related-work comparison, and a pre-registered experimental protocol, but does not claim experimental validation or performance improvements. Those questions are reserved for subsequent papers in the SSQR research series. The proposed framework is intended to provide a falsifiable foundation for future research on adaptive quantum runtime systems, structure-aware error mitigation, and experience-driven execution strategies across NISQ quantum computing platforms.

•Jul 2, 2026•0 citations

Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

Tingting Li, Ziming Zhao, Jianwei Yin

Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.

quant-pharXiv:2601.14713•Jan 22, 2026•0 citations

Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

Tingting Li, Ziming Zhao, Jianwei Yin

Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.

Proceedings of the AAAI Conference on Artificial Intelligence•Mar 14, 2026•0 citations

AutoFid: Adaptive and Noise-Aware Fidelity Measurement for Quantum Programs via Circuit Graph Analysis

Tingting Li, Ziming Zhao, Jianwei Yin

Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.

IEEE/ACM International Conference on Automated Software Engineering (ASE)•Nov 16, 2025•1 citation

Adaptive Noise-Aware Variational Quantum Classifier for Robust Machine Learning on NISQ Devices

Vinodh Kumar S, Pravin P

The execution of Variational Quantum Algorithms on Noisy Intermediate-Scale Quantum hardware is severely impacted by hardware noise variations over time and decoherence. This work introduces an Adaptive Noise-Aware Variational Quantum Classifier (ANAVQC) that integrates Zero-Noise Extrapolation (ZNE) dynamically into the training loop to counter variations. The framework approximates the instantaneous depolarizing noise from calibration and adapts noise scaling and linear extrapolation to estimate noise-free expectation values at training. A two-qubit parameterized quantum circuit is built and simulated in MATLAB, with the inclusion of stochastic gradient descent coupled with parameter-shift-based gradients. The model is conditioned on a synthetic binary classification data under a time-varying depolarizing noise profile and exhibits improved convergence stability and classification accuracy over non-adaptive methods. The simulation results identify that adaptive ZNE increases model robustness against time-varying noise and offers a scalable route to reliable quantum machine learning on practical NISQ hardware. The current ANAVQC scheme is extendable to multi-qubit platforms and practical quantum backends for the purposes of future experimental testing.

International Conference on Intelligent Computing and Automation for Sustainable Solutions (ICASS)•Feb 12, 2026•0 citations
TaiDN-Q is a proposed adaptive runtime framework for quantum experiments in which hardware noise and runtime variation are treated as observable environmental conditions rather than as quantities that must simply be eliminated. The framework separates execution quality from local sensitivity and robustness. A runtime state is represented by Rk = (Xk, Ck, Bk, ηk, Mk, Sk), where Xk denotes the information or problem state, Ck the circuit configuration, Bk the backend state, ηk the noise and environmental state, Mk the learned model, and Sk the measured stability or performance state. A stability score combines fidelity, total variation distance, noise risk, and execution cost. Local noise sensitivity is estimated through finite differences, and the Noise Sensitivity Index (NSI) provides a normalized inverse-sensitivity measure. Robustness is then defined by penalizing local fragility, leading to a stable operating region rather than a single optimum. Finally, an adaptive runtime selects subsequent experiments using a surrogate model and an acquisition function that balances predicted quality, uncertainty, and sensitivity. The central experimentally testable question is whether adaptive search can identify robust quantum execution regimes with fewer quantum experiments than non-adaptive baselines under matched experimental budgets.
We introduce TaiDN-Q, a noise-aware quantum experiment runtime designed for adaptive structured quantum execution on NISQ-era quantum hardware. The framework integrates Information Dynamical Algebra (IDA), tensor-based structured representations, graph parameterized Hamiltonian compilation, adaptive noise modeling, backend independent execution, and reproducible experimental orchestration into a unified scientific runtime architecture. Rather than proposing a universal physical theory, TaiDN-Q is formulated as an experimentally executable infrastructure for transforming structured mathematical systems into adaptive quantum experiments under realistic noisy hardware conditions. The framework defines: • a structured information-dynamical representation layer, • a tensor-to-graph compilation pipeline, • Hamiltonian-based quantum execution, • adaptive noise-aware runtime evolution, • and reproducible benchmark-oriented experiment orchestration. TaiDN-Q extends the Quantum Gateways (QGR) execution architecture toward adaptive runtime-oriented quantum experimentation on IBM Quantum and simulator backends. The framework does not claim quantum supremacy, fault tolerance, emergent spacetime, or universal quantum advantage. Instead, it focuses on executable, observable, adaptive, and reproducible scientific quantum experimentation.
Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing proposes a structure-sensitive runtime architecture for adaptive execution in Noisy Intermediate-Scale Quantum (NISQ) computing. Rather than applying a fixed error mitigation strategy to every quantum circuit, the proposed framework introduces a pre-execution decision layer that analyzes circuit structure and estimates execution reliability before selecting an appropriate mitigation strategy. The paper defines the core concepts of Structure Profile, Structure Fingerprint, Structure Complexity Index (SCI), Runtime Risk, and Confidence Score, and integrates them into a layered runtime architecture consisting of Structure Analysis, Hardware Abstraction, Runtime Intelligence, Adaptive Optimization, and Knowledge layers. This work is intentionally presented as a position and methodology paper. It introduces the mathematical framework, architectural design, theoretical complexity analysis, related-work comparison, and a pre-registered experimental protocol, but does not claim experimental validation or performance improvements. Those questions are reserved for subsequent papers in the SSQR research series. The proposed framework is intended to provide a falsifiable foundation for future research on adaptive quantum runtime systems, structure-aware error mitigation, and experience-driven execution strategies across NISQ quantum computing platforms.
Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.
Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.
Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.
The execution of Variational Quantum Algorithms on Noisy Intermediate-Scale Quantum hardware is severely impacted by hardware noise variations over time and decoherence. This work introduces an Adaptive Noise-Aware Variational Quantum Classifier (ANAVQC) that integrates Zero-Noise Extrapolation (ZNE) dynamically into the training loop to counter variations. The framework approximates the instantaneous depolarizing noise from calibration and adapts noise scaling and linear extrapolation to estimate noise-free expectation values at training. A two-qubit parameterized quantum circuit is built and simulated in MATLAB, with the inclusion of stochastic gradient descent coupled with parameter-shift-based gradients. The model is conditioned on a synthetic binary classification data under a time-varying depolarizing noise profile and exhibits improved convergence stability and classification accuracy over non-adaptive methods. The simulation results identify that adaptive ZNE increases model robustness against time-varying noise and offers a scalable route to reliable quantum machine learning on practical NISQ hardware. The current ANAVQC scheme is extendable to multi-qubit platforms and practical quantum backends for the purposes of future experimental testing.