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This work studies the simplified setting of reinforcement learning with a uniform generative model in which uniformly random state-action samples are available, and provides finite sample guarantees for classical kernelized Fitted Q-Iteration, with classical kernels designed to match the inductive bias of particular pa...
Neural quantum kernels (NQKs) construct quantum kernels by pretraining a quantum neural network (QNN) and subsequently reusing the trained circuit as a task-adapted embedding. Extending this framework to qudits, with local unitaries in $\mathrm{SU}(d)$, provides a natural route to richer data embeddings through the inc...
This work proposes a low-cost criterion for convergence monitoring that exploits the weak measurements inherent in quantum Gibbs samplers and their qubit-efficient variants and constructs a Hamiltonian-agnostic stopping criterion based solely on data already generated by the sampler.
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