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A Hybrid Classical-Quantum Reinforcement Learning Agent for Real-Time Noise Suppression in Quantum Processors

Awad Wehbe, Abdel‐Mehsen Ahmad

Published Dec 14, 2025• 0 citations
Publisher

Abstract

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 Agent${ }^{2}$(HI-QA-DRNA${ }^{2}$), a hybrid classical-quantum framework for the real-time suppression of noise in quantum gate operations. HI-QA-DRNA${ }^{2}$employs 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.

DOI
10.1109/icm66518.2025.11321331

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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{ }^{2}2(HI-QA-DRNA2{ }^{2}2), a hybrid classical-quantum framework for the real-time suppression of noise in quantum gate operations. HI-QA-DRNA2{ }^{2}2employs 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.
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.
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Abstract Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks. To leverage these powerful capabilities for quantum control optimization, we propose a new control framework to simultaneously optimize the speed and fidelity of quantum computation against both leakage and stochastic control errors. For a broad family of two-qubit unitary gates that are important for quantum simulation of many-electron systems, we improve the control robustness by adding control noise into training environments for reinforcement learning agents trained with trusted-region-policy-optimization. The agent control solutions demonstrate a two-order-of-magnitude reduction in average-gate-error over baseline stochastic-gradient-descent solutions and up to a one-order-of-magnitude reduction in gate time from optimal gate synthesis counterparts. These significant improvements in both fidelity and runtime are achieved by combining new physical understandings and state-of-the-art machine learning techniques. Our results open a venue for wider applications in quantum simulation, quantum chemistry and quantum supremacy tests using near-term quantum devices.
The hardware noise and drift, combined with the fragility of stationary hybrid algorithms like the variational quantum eigensolver (VQE), limit the usefulness of noise quantum advantage accessed through Noisy intermediate-scale quantum (NISQ) processors, but allow near-term quantum advantage. In this paper, a Hybrid Quantum-Classical Adaptive Intelligence Framework is suggested, which bridges the gap between quantum hardware, a parameterized quantum circuit, explicit error-mitigation routines, and a controller trained through learning. The quantum subsystem prepares variational states and calculates the expectation values of the problem Hamiltonian, and an AI/ML model approximates an online noise probability signal and moves an optimizer based on reinforcement learning (RL). The RL policy chooses the parameter updates and enables mitigation measures depending on noise-sensitive state functions, such as energy, gradient norm, and estimated noise level. It was demonstrated experimentally using a simulated NISQ-style molecular Hamiltonian that the adaptive controller can reduce the error in the absolute ground state of the system by about 52.8 percent versus a standard static VQE baseline using the same ansatz. The integrated classifier that drives the process of mitigation is accurate with 97 percent accuracy, 100 percent precision, 92 percent recall, 95.9 F1-score, and 99.5 AUC, which shows reliable decision-making with minimal false alarms. All these findings indicate that the close-knit quantum-classical adaptivity can significantly contribute to the robustness and accuracy of VQE-like workloads on NISQ and serve as a scalable template to design noise-aware hybrid algorithms in the future.
The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first agent constructs the qubit in-phase control pulse using a policy learned from rewards that compensate short gate times. The rewards are obtained at intermediate time steps throughout the construction of a full-length pulse, allowing the agent to explore the landscape of shorter pulses. The second agent determines an out-of-phase pulse to target leakage. Both agents are trained on real-time data from noisy hardware, thus providing model-free gate design that adapts to unpredictable hardware noise. To reduce the effect of measurement classification errors, the agents are trained directly on the readout signal from probing the qubit. We present proof-of-concept experiments by designing X and square root of X gates of various durations on IBM hardware. After just 200 training iterations, our algorithm is able to construct novel control pulses up to two times faster than the default IBM gates, while matching their performance in terms of state fidelity and leakage rate. As the length of our custom control pulses increases, they begin to outperform the default gates. Improvements to the speed and fidelity of gate operations open the way for higher circuit depth in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.
Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses a formidable challenge. Using reinforcement learning (RL), we present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. The hybrid policy is modeled by a deep autoregressive neural network to capture causality. We employ the algorithm to prepare the ground state of the nonintegrable quantum Ising model in a unitary process, parametrized by a generalized quantum approximate optimization ansatz: the RL agent solves the discrete combinatorial problem of constructing the optimal sequences of unitaries out of a predefined set and, at the same time, it optimizes the continuous durations for which these unitaries are applied. We demonstrate the noise-robust features of the agent by considering three sources of uncertainty: classical and quantum measurement noise, and errors in the control unitary durations. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.