The paper introduces GenQAS, a tensor‑network‑guided reinforcement learning framework that uses a learned local transition model to generate synthetic circuit transitions for prioritized generative replay. By mixing these synthetic transitions with real experience during Double Deep Q‑Network updates, GenQAS addresses sample starvation in quantum architecture search. Experiments on chemical Hamiltonian benchmarks up to 12 qubits and a 15‑qubit Ising model show significant improvements in success probability and circuit compactness, while a noisy 6‑qubit BeH₂ transfer experiment demonstrates a 92.7% reduction in steps to chemical accuracy.
arXiv:2604.21863v2 Announce Type: replace-cross
Abstract: Deep reinforcement learning for quantum circuit optimization faces three bottlenecks: replay buffers that overlook temporal difference (TD) t...
By Akash Kundu, Sebastian Feld
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: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:2607. 19506v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout.
By Krishna Bhatia, Gautami Sanjay Naik
arXiv:2605. 27410v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage.
By Tung Dao, Son N. Tran, Huynh Thi Thanh Binh