Quantum reinforcement learning is an emerging field at the intersection of quantum
computing and machine learning. While we intend to provide a broad overview of the …

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent
method for optimizing complicated black-box reward functions. Various BO algorithms have …

Here we revisit one of the prototypical tasks for characterizing the structure of noise in
quantum devices: estimating every eigenvalue of an $ n $-qubit Pauli noise channel to error …

Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in
reinforcement learning, and it is well-known that classical algorithms for bandits with time …

The problem of efficient quantum state learning, also called shadow tomography, aims to
comprehend an unknown $ d $-dimensional quantum state through POVMs. Yet, these …

We study a noise model for linear stochastic bandits for which the subgaussian noise
parameter vanishes linearly as we select actions on the unit sphere closer and closer to the …

Can adaptive strategies outperform non-adaptive ones for quantum hypothesis selection?
We exhibit problems where adaptive strategies provably reduce the number of required …

Decision-making enables artificial intelligence to dynamically adjust and acquire knowledge
from experiences, distinguishing it from traditional computing intelligence based on …

In many quantum tasks, there is an unknown quantum object that one wishes to learn. An
online strategy for this task involves adaptively refining a hypothesis to reproduce such an …

One paradigm for machine learning that is transforming is reinforcement learning, or RL,
promising significant healthcare improvements through personalized treatment …