Abstract Realizing scalable fault-tolerant quantum computing (FTQC) has long been constrained by environmental decoherence, forcing conventional quantum error correction (QEC) into massive spatial and temporal overheads. In this work, we introduce QID-OS (Quantum Integrated Dynamic Operating System), a proactive control framework that integrates Recursive Least Squares (RLS) estimation with adaptive scheduling and pulse-level compensation. Rather than reacting to errors post-facto, QID-OS treats environmental noise as a dynamically observable control variable, anticipating fluctuations in real time and applying active compensation prior to error accumulation. QID-OS does not replace conventional fault-tolerant QEC, but rather complements it as a proactive control layer that reduces the effective error burden presented to the downstream QEC stack. Through large-scale numerical simulations under defined phenomenological noise models, QID-OS maintains a logical fidelity of 0.999995 (evaluated via Monte Carlo state-overlap estimators over 105 independent Monte Carlo trajectories) where the conventional baseline collapses. Furthermore, completed hardware validation on IBM Quantum processors demonstrates that the predictive RLS and pulse-compensation components of QID-OS successfully execute a 5-qubit Greenberger-Horne-Zeilinger (GHZ-5) state, improving fidelity from a standard baseline of 68.5% to 88.4% (a +19.9 percentage-point improvement). These results establish QID-OS as a powerful proactive control architecture for noisy quantum execution.
Abstract Realizing scalable fault-tolerant quantum computing (FTQC) has long been constrained by environmental decoherence, forcing conventional quantum error correction (QEC) into massive spatial and temporal overheads. In this work, we introduce QID-OS (Quantum Integrated Dynamic Operating System), a proactive control framework that integrates Recursive Least Squares (RLS) estimation with adaptive scheduling and pulse-level compensation. Rather than reacting to errors post-facto, QID-OS treats environmental noise as a dynamically observable control variable, anticipating fluctuations in real time and applying active compensation prior to error accumulation. QID-OS does not replace conventional fault-tolerant QEC, but rather complements it as a proactive control layer that reduces the effective error burden presented to the downstream QEC stack. Through large-scale numerical simulations under defined phenomenological noise models, QID-OS maintains a logical fidelity of 0.999995 (evaluated via Monte Carlo state-overlap estimators over 105 independent Monte Carlo trajectories) where the conventional baseline collapses. Furthermore, completed hardware validation on IBM Quantum processors demonstrates that the predictive RLS and pulse-compensation components of QID-OS successfully execute a 5-qubit Greenberger-Horne-Zeilinger (GHZ-5) state, improving fidelity from a standard baseline of 68.5% to 88.4% (a +19.9 percentage-point improvement). These results establish QID-OS as a powerful proactive control architecture for noisy quantum execution.
Quantum processors exhibit time-varying hardwaredrift, environmental fluctuations, and correlated control errorsthat challenge reliable execution and increase the physical errorburden presented to higher-level error-correction procedures.Existing approaches address important aspects of quantumcalibration, feedback control, scheduling, and error correction,but these functions are typically studied and implemented asdistinct components. We introduce QID-OS (Quantum Intelligent Dynamic Operating System), an operating-system-levelcontrol-plane architecture for quantum execution that mediatesbetween physical hardware telemetry and higher-level compilation, runtime, and quantum error-correction services. QID-OSintegrates causal recursive least-squares (RLS) online parameterestimation, predictive pulse-level compensation, and adaptiveexecution scheduling within a closed-loop runtime architecture.The proposed framework is evaluated using a distance-3 surfacecode simulation under a specified non-Markovian noise model, afull-factorial 23 module ablation, and experiments on a 5-qubitconnected subset of an IBM Quantum superconducting processor.In simulation, QID-OS improves logical success probability overthe evaluated noise conditions, and the full configuration achievesthe highest success probability among the evaluated modulecombinations. On superconducting hardware, the experimentallyvalidated online RLS estimation and active pulse-compensationcomponents increase computational-basis success probabilityfrom 68.5% ± 0.4% to 88.4% ± 0.3% across three independent8-hour calibration windows, comprising 300 randomized testcircuits and 2,457,600 total measurement shots. These resultsdemonstrate the feasibility of using a predictive control-planearchitecture to mitigate hardware-level errors prior to higherlevel quantum error-correction processing.
The promise of quantum computing is closer to reality today than ever before, thanks to rapid progress in the development of quantum hardware. Even as qubit lifetimes and gate fidelities continue to improve, realizing robust, fault-tolerant quantum computers is contingent upon the successful implementation of quantum error correction (QEC). Conventional QEC schemes have rather high resource overheads and low threshold requirements, making them challenging to implement on present day hardware. Here, we use a recently developed noise-adapted 3-qubit QEC scheme to demonstrate break-even performance against native amplitude-damping (AD) noise on IBM quantum hardware. We use variational quantum circuits to construct hardware-efficient encoding and decoding circuits. This scheme is probabilistic due to the non-unitary nature of the recovery operators, which are implemented via the block-encoding technique. We demonstrate logical qubit lifetimes exceeding those of the physical qubits by performing multiple rounds of QEC. To further protect the qubits from dephasing due to crosstalk, we incorporate dynamical decoupling into our noise-adapted QEC scheme in a seamless fashion. To account for the post-selection overhead, we define a measure of gain, that allows for faithful performance benchmarking of the protocol. Our analysis suggests that the performance of our protocol is limited primarily by the measurement readout fidelity, and is bound to improve with successive generations of quantum processors.
This paper presents a novel approach to quantum error correction (QEC) that integrates robust coding schemes with adaptive control strategies for mitigating decoherence, a primary obstacle to large-scale quantum computation. The core concept involves treating decoherence not as a static noise source, but as a dynamic and time-varying process. We introduce a new QEC scheme which incorporates a machine learning algorithm to predict and compensate for decoherence effects in real-time. The system dynamically adjusts control parameters, such as pulse shapes and timings, based on these predictions to minimize errors. Our approach significantly enhances the resilience and computational efficiency of quantum systems by offering a proactive and adaptive response to the inherent instability of quantum states. The theoretical framework and underlying mathematical models are presented, demonstrating the potential for substantial improvements in quantum computation.
Quantum error correction (QEC) faces a significant challenge in realistic noisy intermediate-scale quantum (NISQ) devices: correlated decoherence. Building upon the foundational QEC-C protocol, we introduce QEC-C II, a scalable framework that delivers robust fault tolerance against spatially and temporally correlated noise. By integrating decoherence-free subspace (DFS) principles and correlation-aware adaptive feedback, QEC-C II achieves exponential error suppression in simulation under correlated noise models, with fewer physical qubits than typical topological baselines for the same target error in our benchmarks. The protocol leverages an enhanced scaling law, β(n) = 0.788 e 0.360n , and efficiently suppresses noise with minimal computational overhead. We validate QEC-C II on 28-qubit GHZ states, extend it to 56-qubit subspaces, and conduct 84-qubit stress-test simulations under correlated noise models. The results demonstrate sustained fidelities exceeding 99.99 % in simulations under realistic dephasing, amplitude-damping, burst-error, and correlated decoherence scenarios, with rapid convergence (typically ≤ 2 iterations in our simulations). QEC-C II significantly reduces both resource and control complexity, simplifying hardware integration and improving performance with increasing system size. This work provides a practical pathway toward resource-efficient fault tolerance in quantum processors operating in real-world correlated environments.
In realistic hardware for quantum computation that possesses fault-tolerance, non-stationary noise and stochastic drift lead to logical failure from the temporal accumulation of errors, not from independent events. Static decoding and fixed calibration techniques are structurally incompatible with this situation because they do not take into account temporal correlations between errors or control-induced back-action of errors. These effects motivate control policies that must track noise evolution across correction cycles, rather than respond to individual syndromes in isolation.
We treat fault-tolerant quantum computation as a stochastic control problem, modelled using reduced quantum dynamics in which Pauli error processes are governed by latent noise parameters that vary temporally. From this perspective, logical failure arises through the accumulation of a hazard variable, and the corresponding control objective depends on the full history of observations.
Operating under these conditions, a Chronological Deep Q-Network (Ch-DQN) maintains an internal belief state that tracks both noise evolution and accumulated hazard. During training, backward refinement of trajectories is used to sample slowly drifting modes of operation, while runtime inference remains strictly causal. A fractional meta-update stabilizes learning in the presence of non-stationary, control-coupled dynamics.
Through multi-distance simulations that incorporate stochastic drift and feedback from decision-making, Ch-DQN suppresses hazard accumulation and extends logical survival time relative to static and recurrent baselines. Error correction in this regime is therefore no longer a static decoding task, but a control process whose success is determined over time by the underlying noise dynamics.
Correlated noise significantly hampers qubit stability and computational precision in quantum computing. Traditional error mitigation techniques assume independent noise and fail to adapt dynamically to evolving error patterns. To address this, we propose an AI-driven noise reduction framework that employs a Recurrent Neural Network (RNN)-Transformer architecture, achieving 92.4% accuracy in real-time noise prediction. Integrated federated learning enables secure, cross-device adaptation while preserving user privacy. Additionally, Bayesian optimization and adaptive gate reconfiguration reduce logical error rates by43%and improve gate fidelity by35%, enhancing overall computational reliability. Using IBM Quantum Falcon hardware, the proposed framework demonstrated superior stability and noise suppression compared to conventional methods. Through simulation and partial hardware validation, the reinforcement learning (RL) component effectively adapted error-correction strategies under varying noise conditions, ensuring continuous optimization. The framework's scalability and hardware independence were further confirmed through successful deployment on IBM Qiskit and Google Cirq, highlighting adaptability across heterogeneous quantum architectures. This study lays a robust foundation for advancing stable, accurate, and practical quantum computation.