arXiv AI
Jun 12
SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
arXiv:2606. 12808v1 Announce Type: cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices.
SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation.
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arXiv Machine Learning
Jul 1
Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
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 AI
Aug 3
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
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.
arXiv Machine Learning
Jun 19
Optimal Ansatz-free Hamiltonian Learning In Situ
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.
arXiv AI
Jul 20
A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model
arXiv:2507. 22854v3 Announce Type: replace-cross Abstract: We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs).
Hugging Face Trending Papers
Jun 11
Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by standard overhead metrics can still lose fidelity when they pass through poorly calibrated couplers.
arXiv AI
Jul 10
Spectral Analysis of Dueling Q-Learning
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.
arXiv Machine Learning
Jul 3
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
arXiv:2607. 02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions.
arXiv Machine Learning
Jun 18
Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
arXiv:2606. 12816v2 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors.
Hugging Face Trending Papers
Jul 2
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods.
arXiv Machine Learning
Jun 18
Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction
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.
arXiv AI
Jul 22
Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions
arXiv:2509. 08654v2 Announce Type: replace-cross Abstract: Quantum network routing requires online decisions under probabilistic entanglement generation, finite quantum memories, decoherence, imperfect operations, and classical feedback, while the controller has incomplete knowledge of the physical state.