Adaptive error mitigation improves learning on noisy quantum devices
Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems
Machine Learning
Summary
Quantum computers that exist today are still noisy and make errors, which makes training quantum algorithms harder. This paper presents a new way to pick the best error-correcting method during the learning process, adjusting dynamically as conditions change. The method helps quantum reinforcement learning perform better on practical problems despite noise. The authors tested it on a vehicle routing challenge and found it keeps learning more stable and accurate than fixed error fixes. This approach could help noisy quantum computers run smarter algorithms more reliably.
What this means in practice
- •For quantum algorithm developers: Improve reliability of quantum reinforcement learning algorithms on noisy intermediate-scale quantum hardware.
- •For urban logistics planners: Use hybrid quantum-classical approaches to tackle complex routing problems more robustly despite quantum hardware noise.
Authors
Bisma Majid, Shabir Ahmed Sofi, Mir Mohammad Yousuf
Abstract
Quantum Reinforcement Learning (QRL) integrates reinforcement learning with parameterized quantum circuits and is a promising approach to combinatorial optimization. On Noisy Intermediate-Scale Quantum (NISQ) devices, however, decoherence, gate imperfections, and measurement errors reduce policy quality and make learning less reliable. Existing error mitigation techniques are generally applied as fixed corrections that do not adapt to changing noise conditions or to the evolving state of training. This work presents Adaptive Policy-Guided Error Mitigation (APGEM) as a context-aware orchestration layer of the hybrid quantum-classical training loop that dynamically selects the most suitable mitigation strategy during QRL training. APGEM evaluates Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) using policy-level indicators, including quantum-state fidelity, policy entropy, cumulative reward, and approximation ratio, and integrates the selected strategy directly into the reinforcement learning loop. The framework is evaluated on the Capacitated Vehicle Routing Problem (CVRP), a representative NP-hard problem in urban logistics, under a range of NISQ noise models and noise levels. APGEM consistently outperforms conventional static mitigation methods, reaches approximately 94% of the utility of an oracle strategy, maintains higher quantum-state fidelity as noise increases, and produces more stable learning behaviour throughout training. Ablation studies show that the framework learns context-aware mitigation policies that adapt to different noise environments and circuit execution conditions. These findings demonstrate that integrating adaptive error mitigation into the learning process substantially improves the robustness and reliability of QRL on NISQ hardware.