The paper introduces APGEM, an adaptive controller that dynamically selects among four error‑mitigation techniques—Zero‑Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation—based on a utility function and Q‑learning scores. Applied to a realistic Delhi‑based Capacitated Vehicle Routing Problem, the adaptive approach improves the quantum reinforcement learning agent’s approximation ratios from 0.84‑0.87 to 0.92‑0.94 under high noise, outperforming constructive heuristics and approaching metaheuristics. The controller’s strategy shifts from a Clifford‑data‑regression‑heavy regime early in training to a balanced use of all techniques as training progresses, demonstrating regime‑dependent selection.
By Shabir Ahmad Sofi, Bisma Majid, Mir Mohammad Yousuf
The paper introduces Adaptive Policy-Guided Error Mitigation (APGEM), a context-aware layer that dynamically selects error mitigation strategies—such as ZNE, PEC, CDR, and REM—during quantum reinforcement learning (QRL) training on NISQ devices. APGEM uses policy-level indicators (quantum-state fidelity, policy entropy, cumulative reward, and approximation ratio) to choose the most suitable mitigation method and integrates it directly into the reinforcement learning loop. Evaluated on the Capacitated Vehicle Routing Problem under various NISQ noise models, APGEM outperforms static mitigation techniques, achieving about 94% of an oracle strategy’s utility, maintaining higher fidelity as noise increases, and producing more stable learning behavior.
By Bisma Majid, Shabir Ahmed Sofi, Mir Mohammad Yousuf
arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.
By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.
By Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa
The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.
By Hoong Chuin LAU
arXiv:2606. 10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC).
By Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai, Yuanchen Gao, Xiaoyi Pang, Hualei Zhang, Jingcai Guo, Jie Zhang, Song Guo
arXiv:2606. 08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic environments.
By Alexander DeRieux, Walid Saad
arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.
By Manoranjan Gandhudi, Arunkumar V., G. R. Anil, Gangadharan G. R
The paper introduces QEMScore, a metric that compares learned quantum error mitigators to capacity‑matched controls that do not use measurement data. Using simulated circuits with exact ideal answers, the study finds that many mitigators gain little from measurement inputs, with a plain polynomial model often outperforming them. On real hardware data, however, measurement inputs can provide predictive benefits, highlighting that performance depends on representation and protocol specifics.
By Yue Zhao, Huayue Gu, Yushun Dong, Xiyang Hu
arXiv:2607. 01197v1 Announce Type: new Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches.
By Chuanming Yu, Jiaming Liu, Zihao Ge, Xiongfei Wu, Lulu Zhu, Pengzhan Zhao, Jianjun Zhao
arXiv:2606. 09778v1 Announce Type: cross Abstract: Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time.
By Yifan Wang
The paper studies how an eavesdropper can adaptively attack quantum key distribution (QKD) systems when channel noise and device drift vary over time. By modeling the attack as a constrained Markov decision process and using reinforcement learning to jointly search gate structures and rotation angles, the authors construct compact attack circuits that perform near the theoretical upper bound for both device‑independent E91 and BB84 protocols under realistic noise models. The results show that adaptive attacks can significantly increase the eavesdropper’s information compared to fixed‑circuit strategies, and that the learned attacks recover known optimal cloners and key‑rate bounds.
By Marcel Mordarski, Benjamin Gras, Abdelrahman Shehata, Daniel Budina, Roberto Bondesan