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.
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.
Noisy Intermediate-Scale Quantum (NISQ) devices are promising platforms for practical quantum computing, but their performance is significantly affected by limited qubit availability, gate errors, decoherence, circuit depth, and hardware-dependent noise. Conventional quantum circuits are generally designed and optimized before execution and may not respond effectively to changing hardware conditions during computation. This research proposes a Self-Adaptive Quantum Circuit framework that dynamically modifies quantum circuits according to the current computational resources and noise characteristics of NISQ devices. The proposed framework monitors parameters such as qubit fidelity, gate error rates, circuit depth, execution time, and resource availability, and automatically selects suitable circuit configurations to improve computational performance. Adaptive mechanisms can be employed to reduce unnecessary gates, optimize qubit utilization, and mitigate the impact of hardware noise while maintaining the required computational accuracy. The framework will be evaluated using simulation platforms and available NISQ quantum hardware, comparing adaptive circuits with conventional static circuits using metrics such as accuracy, fidelity, circuit depth, gate count, execution time, and error rate. The expected outcome is a resource-efficient and noise-aware quantum circuit optimization approach capable of improving the reliability and performance of quantum applications on near-term quantum hardware. The proposed research aims to contribute toward more practical, flexible, and hardware-aware quantum computing systems for NISQ-era applications. Keywords: NISQ Computing, Self-Adaptive Quantum Circuits, Quantum Circuit Optimization, Quantum Error Mitigation, Quantum Noise, Quantum Computing.
The rapid evolution of Noisy Intermediate-Scale Quantum (NISQ) devices has intensified the need for robust compilation strategies capable of mitigating hardware-induced errors while maximizing computational efficiency. Quantum compilers play a pivotal role in translating high-level quantum algorithms into hardware-executable instructions, yet they face significant challenges related to qubit connectivity constraints, limited coherence times, and stochastic noise. This paper examines error-resilient quantum compiler design for efficient qubit mapping, gate optimization, and noise mitigation in NISQ-era devices, proposing a framework that integrates topology-aware mapping, adaptive gate synthesis, and probabilistic error suppression techniques. By synthesizing advances in quantum circuit optimization, hardware-aware scheduling, and hybrid classical–quantum error correction strategies, the study evaluates how compiler-level innovations can substantially enhance execution fidelity and resource efficiency. The findings suggest that intelligent compiler architectures are critical enablers of practical quantum advantage during the transitional NISQ phase, bridging theoretical algorithm design with real-world hardware constraints.
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.
The development of quantum computing relies heavily on the effective execution of quantum algorithms. However, current Noisy Intermediate-Scale Quantum (NISQ) computers are plagued by limitations such as short coherence times and significant error rates, posing substantial challenges to scheduling and execution. This paper introduces a novel dynamic resource-aware scheduling algorithm designed to mitigate these challenges. The core of the algorithm lies in its ability to adapt to the fluctuating noise levels and coherence times of the quantum hardware, intelligently prioritizing tasks based on their estimated computational cost and error probability. We demonstrate that this adaptive approach significantly improves the potential for successful quantum algorithm execution on NISQ devices, offering a pathway towards more efficient and reliable quantum computation. The algorithm's effectiveness is evaluated through theoretical analysis and conceptual modeling, focusing on the optimization of resource allocation and task prioritization within the constraints of NISQ technology. This research contributes to the growing field of quantum resource management and scheduling, providing a framework for tackling the complexities inherent in operating quantum computers.
Quantum computing on Noisy Intermediate-ScaleQuantum (NISQ) devices is fundamentally limited by noise, ne-cessitating effective error mitigation techniques. Existing methodssuch as Zero Noise Extrapolation (ZNE) including linear andRichardson extrapolation variants exhibit varying performanceacross circuits and noise regimes, with no single method univer-sally optimal. In this work, we propose a regime-aware adaptiveframework that leverages machine learning to dynamically selectthe most effective mitigation strategy for a given circuit and noisecondition. Two XGBoost regressors independently predict theexpected improvement of Linear ZNE and Richardson ZNE, andthe method with the higher predicted score is selected at runtime.The selector achieves 64.3% exact accuracy, representing an 11.9percentage point improvement over the strongest fixed-methodbaseline. The adaptive selector outperforms Richardson ZNEon all four benchmark circuits and matches or exceeds bothbaselines on structured, deeper circuits. These results highlightthe importance of data-driven, regime-aware mitigation selectionfor improving the reliability of near-term quantum computations.
Abstract Suppressing errors is one of the central challenges in achieving reliable quantum computation on near-term hardware. While much attention has been paid to error correction and mitigation, we identify quantum circuit structure itself as a powerful lever for proactive error suppression. In this work, we present a unified and hardware-adaptive framework for noise-aware quantum circuit design, in which circuit topology and parameters are co-optimized from the ground up based on the noise profile of the target backend. Our framework supports a wide range of quantum tasks-including circuit compilation, quantum state preparation, and unitary approximation-under a consistent optimization paradigm. Extensive experiments on five IBM backends confirm the effectiveness of our method, showing significant fidelity gains across all tasks under realistic noise. These results demonstrate that noise suppression through structure-aware design offers a powerful and generalizable strategy for enhancing circuit performance on NISQ hardware. Our framework bridges low-level hardware constraints with high-level circuit synthesis, paving the way for more robust and efficient quantum programming in the presence of noise.