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
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
Titans-QFWP is a hybrid reinforcement learning architecture that combines a Quantum Fast Weight Programmer with a Titans-style memory system (Persistence, Surprise, and Forgetting) for adaptive portfolio optimization. It employs an enhanced A3C² framework with Hungarian-aligned K‑means clustering and scaled log‑return rewards to handle high‑dimensional market features. Tested on 468 S&P 500 stocks with about 3,000 trainable parameters, the model achieves strong performance metrics (median ARR 0.4260, Calmar 8.5504, IR 0.8427) and demonstrates that quantum gating reshapes memory roles to support drawdown control, return generation, and stabilization.
By Ming-Kai Hung, Jun-Hao Chen, Yun-Cheng Tsai, Samuel Yen-Chi Chen
arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.
By Peiyong Wang, Kieran Hymas, James Quach
arXiv:2607. 21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed.
By Marco Mordacci, Michele Amoretti
arXiv:2608. 15715v1 Announce Type: cross Abstract: Quantum feedback control requires acting on noisy continuous measurement records without direct access to the underlying quantum state.
By Priyanshi Singh, Krishna Bhatia