Hybrid quantum-classical adaptive intelligence framework for real-time optimization in noisy intermediate-scale quantum (NISQ) systems
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Full Text |
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Author |
Kondamudi Sivaparvathi, C. Prabhavathi and B. Muni Lavanya
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e-ISSN |
1819-6608 |
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On Pages
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617-629
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Volume No. |
21
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Issue No. |
9
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Issue Date |
July 10, 2026
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DOI |
https://doi.org/10.59018/052668
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Keywords |
NISQ systems, hybrid quantum-classical computing, variational quantum eigensolver (VQE), reinforcement learning, adaptive optimization, quantum error mitigation.
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Abstract
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.
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