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 by$\mathbf{4 3 \%}$and improve gate fidelity by$\mathbf{3 5 \%}$, 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.
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.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
The theoretical impediments of quantum computing to the large-scale implementation are inherently noise, decoherence, and inefficiency of the current fault-tolerant schemes. Although quantum hardware and error detection methods have made great advancements, existing methods largely use fixed logic qubit simulations and fixed noise models, which are inefficient at responding to dynamically changing noise models and do not scale. To curb this shortcoming, there is a need to have a framework that incorporates flexibility, statistical intelligence, and system optimization. In this paper, a dynamically adaptive logic qubit architecture is proposed based on a statistical model that can be used to achieve fault-tolerant quantum computing on a scale. The framework makes use of probabilistic noise modeling, real-time feedback-based reconfiguration in the continuous optimization of logical qubit structures and error correction schemes. The proposed architecture reduces the error propagation and reduces resource overhead by adding statistical inference using adaptive control mechanisms. Large-scale simulation experiments show that the given approach can suppress logical errors up to 28 percent better and reduce qubit overhead by 35 percent in comparison to the traditional fault-tolerant models that are based on static models and fault-tolerance in different noise levels. Moreover, the framework has a stable performance when the size of the system is growing, which indicates its stability and scalability. These results put dynamically adaptive logical qubit architectures as a potentially fruitful direction towards real-world and scalable quantum computing, and can be used in optimization, secure communication, and modeling of complex systems.
The fragility of quantum states to environmental noise presents a fundamental challenge for scalable quantum computing. While static error correction and optimal control pulses offer some mitigation, they lack the adaptability to counter time-varying noise. This paper introduces the HardwareInformed, Quantum-Aware, Deep Recurrent Neural-network Agent2(HI-QA-DRNA2), a hybrid classical-quantum framework for the real-time suppression of noise in quantum gate operations. HI-QA-DRNA2employs a classical deep reinforcement learning agent, built on a Gated Recurrent Unit (GRU)Transformer architecture, to process a temporal sequence of noise observations. The agent predicts corrective parameters that dynamically adjust a base control pulse, which is then executed on a simulated, hardware-aware quantum processor. Crucially, the agent is trained using a composite reward function based on quantum information-theoretic metrics-state fidelity, von Neumann entropy, and Quantum Fisher Information (QFI)-that incentivizes not just accuracy but also state purity and robustness. This work demonstrate a significant performance advantage over an optimized static gate. On a test set of time-varying noise trajectories, The agent achieves a 74.6 % improvement in state purity and a 268.7 % improvement in robustness (1/QFI), with a modest 0.8 % gain in state fidelity. This multi-metric improvement underscores the value of the proposed holistic training framework for developing noise-resilient quantum control policies.
Quantum computing holds the potential to revolutionize computation by solving complex problems that classical computers cannot. However, quantum systems are highly susceptible to errors due to noise and decoherence. In this paper, we propose a novel hybrid AI-Quantum approach for Quantum Error Correction (QEC) to optimize noise reduction techniques. By leveraging deep learning and reinforcement learning, we develop a method to predict and correct quantum noise in real time. Our experiments, conducted on IBM Qiskit simulators and actual quantum processors, demonstrate a significant reduction in quantum gate errors compared to traditional QEC codes. Our method achieves improved fault tolerance with reduced qubit overhead, paving the way for scalable and reliable quantum computation. Furthermore, we make our research publicly available with open-source Python code and an interactive Jupyter notebook, enabling others to replicate and extend our work.
Quantum error mitigation (QEM) is a crucial step in achieving fault-tolerant quantum computation. Current methods often rely on fixed noise shaping, which struggles to adapt to the dynamic nature of quantum systems. This paper proposes a novel QEM technique leveraging reinforcement learning (RL) to dynamically adjust noise shaping parameters based on observed error rates. The core mechanism involves an RL agent that learns optimal noise shaping strategies to minimize errors while maintaining accuracy, offering a continuous adaptation to error conditions. This approach significantly improves the robustness and efficiency of QEM.