arXiv:2606. 12808v1 Announce Type: cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices.
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Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation.
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.
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.
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. Across benchmarks ranging from 6 to 15 qubits, the method improves success probabilities, identifies compact circuits, and reduces steps to chemical accuracy by up to 92.7%.
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...
arXiv:2606. 19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction.