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.
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.
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.
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.
Abstract The arrival of noisy intermediate-scale quantum (NISQ) devices defines an era in which quantum utility is fundamentally constrained by finite coherence times, limited qubit counts, gate infidelities, restricted connectivity, and measurement noise. Within this regime, hybrid quantum-classical frameworks have emerged as the dominant operational paradigm, particularly for quantum machine learning (QML). This review identifies a fundamental trade-off between expressivity, trainability, and noise resilience that governs the performance and scalability of NISQ-era QML systems. To contextualize these challenges, we introduce the resource-constrained iterative routing framework, a hardware-aware conceptual architecture that integrates topology-aware classical compression and distributed quantum orchestration to maintain circuit depths within device-dependent limits while routing representations toward symmetry-constrained Hilbert subspaces to mitigate noise-induced barren plateaus. Furthermore, we shift the discussion of quantum advantage away from asymptotic fault-tolerant complexity toward the notion of operational NISQ advantage based on finite-resource scaling, trainability preservation, and sampling complexity. We evaluate the practical resource efficiency of variational quantum algorithms, quantum kernel methods, and quantum neural networks across superconducting, trapped-ion, neutral-atom, and photonic platforms. The review also examines reproducibility challenges, emphasizing benchmarking against optimized classical baselines, and discusses strategies for managing the sampling overhead of error mitigation alongside distributed execution and quantum circuit compilation. Ultimately, NISQ in Practice positions hybrid QML as a physically constrained computational paradigm while outlining a pathway from contemporary noisy workflows toward early fault-tolerant quantum architectures.
A massive gap exists between current quantum computing (QC) prototypes, and the size and scale required for many proposed QC algorithms. Current QC implementations are prone to noise and variability which affect their reliability, and yet with less than 80 quantum bits (qubits) total, they are too resource-constrained to implement error correction. The term Noisy Intermediate-Scale Quantum (NISQ) refers to these current and near-term systems of 1000 qubits or less. Given NISQ's severe resource constraints, low reliability, and high variability in physical characteristics such as coherence time or error rates, it is of pressing importance to map computations onto them in ways that use resources efficiently and maximize the likelihood of successful runs. This paper proposes and evaluates backend compiler approaches to map and optimize high-level QC programs to execute with high reliability on NISQ systems with diverse hardware characteristics. Our techniques all start from an LLVM intermediate representation of the quantum program (such as would be generated from high-level QC languages like Scaffold) and generate QC executables runnable on the IBM Q public QC machine. We then use this framework to implement and evaluate several optimal and heuristic mapping methods. These methods vary in how they account for the availability of dynamic machine calibration data, the relative importance of various noise parameters, the different possible routing strategies, and the relative importance of compile-time scalability versus runtime success. Using real-system measurements, we show that fine grained spatial and temporal variations in hardware parameters can be exploited to obtain an average 2.9x (and up to 18x) improvement in program success rate over the industry standard IBM Qiskit compiler. Despite small qubit counts, NISQ systems will soon be large enough to demonstrate "quantum supremacy", i.e., an advantage over classical computing. Tools like ours provide significant improvements in program reliability and execution time, and offer high leverage in accelerating progress towards quantum supremacy.