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arXiv:2609.18219

APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study

Shabir Ahmad Sofi, Bisma Majid, Mir Mohammad Yousuf

Published Sep 16, 2026• 0 citations
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Abstract

Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization such as the Capacitated Vehicle Routing Problem (CVRP). On noisy intermediate-scale quantum (NISQ) hardware, however, decoherence degrades fidelity and destabilizes learning, and conventional error mitigation is applied statically without regard to the learning context. We introduce Adaptive Policy-Guided Error Mitigation (APGEM), a controller that selects among Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) online, driven by a fidelity, entropy, and cost aware utility function and an epsilon-greedy rule over temporal-difference Q-scores. We evaluate on a realistic urban-logistics testbed, a Delhi-based CVRP over real landmarks with geodesic inter-node costs, exercised across five noise families and four severity levels. On this instance, the QRL agent outperforms constructive heuristics and approaches metaheuristics, while mitigation restores approximation ratios from 0.84-0.87 to 0.92-0.94 under high noise. The controller shifts from a CDR-dominated regime under short training horizons to a balanced deployment across all four techniques under longer horizons, indicating genuine regime-dependent selection. These preliminary results position adaptive, learning-aware mitigation as a practical route to noise-resilient QRL.

DOI
10.48550/arxiv.2609.18219

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Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization such as the Capacitated Vehicle Routing Problem (CVRP). On noisy intermediate-scale quantum (NISQ) hardware, however, decoherence degrades fidelity and destabilizes learning, and conventional error mitigation is applied statically without regard to the learning context. We introduce Adaptive Policy-Guided Error Mitigation (APGEM), a controller that selects among Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) online, driven by a fidelity, entropy, and cost aware utility function and an epsilon-greedy rule over temporal-difference Q-scores. We evaluate on a realistic urban-logistics testbed, a Delhi-based CVRP over real landmarks with geodesic inter-node costs, exercised across five noise families and four severity levels. On this instance, the QRL agent outperforms constructive heuristics and approaches metaheuristics, while mitigation restores approximation ratios from 0.84-0.87 to 0.92-0.94 under high noise. The controller shifts from a CDR-dominated regime under short training horizons to a balanced deployment across all four techniques under longer horizons, indicating genuine regime-dependent selection. These preliminary results position adaptive, learning-aware mitigation as a practical route to noise-resilient QRL.
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Near-term quantum processors operate in the noisy intermediate-scale quantum (NISQ) region, where it is impractical to implement fault-tolerant error correction. Quantum Error Mitigation (QEM) provides a practical alternative, but most existing approaches rely on computationally intensive classical resources which are impractical to implement in embedded or edge-controlled systems. This work presents an adaptive, hardware-aware QEM framework designed for resource-constrained classical processors, demonstrated using a Raspberry Pi 5 as an edge controller for IBM Quantum hardware. A deep neural network predicts circuit fidelity and entropy from circuit and hardware features, enabling an online fuzzy-logic policy selector to choose among no mitigation, dynamical decoupling, and zero-noise extrapolation. Using real hardware feedback, an adaptive bias mechanism update policy was developed. The system operates without full-state simulation or offline recalibration and is evaluated on both randomized and benchmark-referenced circuits. Results show stable mitigation behaviour across depth regimes, primarily preventing fidelity degradation and selectively improving output similarity where mitigation is effective. This work demonstrates the feasibility of adaptive QEM deployment on classical edge devices under strict resource constraints.
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Variational quantum algorithms (VQAs) are promising for near-term noisy intermediate-scale quantum (NISQ) devices, but their performance is often limited by device noise, finite-shot uncertainty, and nonstationary behavior that destabilize optimization. This paper proposes a cost-aware closed-loop hybrid framework (Adaptive V16) that augments a standard VQA loop with an online decision policy to adapt measurement effort and mitigation actions during execution. At each iteration, the controller uses runtime signals such as objective progress and uncertainty to select an action comprising the shot budget, mitigation mode, and step-scaling factor, explicitly trading solution improvement against resource overhead under a fixed budget. Experimental evaluation on a representative molecular Hamiltonian optimization task shows that Adaptive V16 achieves a lower final energy than a static VQE baseline and reduces the absolute error from 0.1391 to 0.0657. The controller’s decision module also demonstrates strong predictive performance with 0.9700 accuracy, 0.9595 F1-score, and 0.9951 ROC–AUC, indicating reliable adaptive behavior across thresholds. These results suggest that policy-driven adaptation can improve robustness and budget efficiency for hybrid quantum–classical optimization in realistic NISQ settings.
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