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International Conference on Information Science and Communication Technology (ICISCT)

Zero-Noise Quantum Reinforcement Learning: Error-Resilient Learning Architectures for Scalable Quantum Control Systems

Kainat Rizwan

Published Apr 15, 2026• 0 citations
Publisher
DOI
10.1109/icisct67740.2026.11576471

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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.
Modern cloud systems demand efficient task scheduling to optimize resource utilization and meet QoS requirements amid unpredictable workloads. Classical Reinforcement Learning (RL) struggles with slow convergence and poor scalability in these complex environments due to exponentially growing state-action spaces. Quantum Reinforcement Learning (QRL) addresses these limitations by exploiting superposition and entanglement to enable parallel policy exploration and faster learning. This survey analyzes recent QRL and quantum-inspired schedulers across cloud, edge, and fog computing, highlighting their superior performance over classical Deep Reinforcement Learning (DRL) baselines in simulated settings. Key findings reveal persistent methodological gaps, including an exclusive reliance on noise-free quantum simulators, an absence of real hardware validation, and limited scalability testing. Future research directions include prioritizing noise-resilient designs, developing hybrid quantum-classical systems for NISQ-era deployment and integrating data-aware scheduling algorithms.
Quantum error correction (QEC) is needed for building reliable and scalable quantum computers because of the susceptibility of quantum states to decoherence and operational noise. Reinforcement learning (RL) has recently emerged as an adaptive approach for quantum control and error mitiga- tion;however, existing RL-based QEC methods are largely re- stricted to small qubit systems and are often dependent on specific hardware architectures. We present a RL–enabled QEC ap- proach for a five qubit quantum system. The proposed approach relies exclusively on syndrome measurements and reward feed- back, ensuring architecture independence and portability across quantum platforms. The framework is evaluated using Qiskit and Cirq simulators under realistic noise models. Experimental results demonstrate improved logical qubit lifetimes and reduced error rates compared to static and smaller qubit correction strategies.