1 citations · 1 across the 8 of their papers we have counts for
9 papers
An Irreducible Quantum Advantage in Aligning World Models with Reality
Josep Lumbreras, Hailan Ma, Jayne Thompson +1
World models provide digital simulacra of the true world, allowing agents to be trained and tested before costly real-world deployment. At each time step, they receive an action an…
Learning Pure Quantum States in Any Dimension (Almost) Without Regret
Josep Lumbreras, Marco Tomamichel
We extend quantum state tomography with minimal cumulative disturbance, first investigated in [arXiv:2406.18370], to arbitrary finite-dimensional pure states. A learner sequentiall…
Quantum Tilted Loss in Variational Optimization: Theory and Applications
Yixian Qiu, Josep Lumbreras, Xiufan Li +1
Variational quantum algorithms (VQAs) are leading strategies for using near-term quantum devices, with a well-studied bottleneck being their trainability. Standard expectation-valu…
Reinforcement learning for quantum processes with memory
Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu +2
In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental expl…
Bandits roaming Hilbert space
Josep Lumbreras
This thesis studies the exploration and exploitation trade-off in online learning of properties of quantum states using multi-armed bandits. Given streaming access to an unknown qu…
Quantum state-agnostic work extraction (almost) without dissipation
Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu +2
We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to copies of an unknown pure qubit state. The cor…
Learning pure quantum states (almost) without regret
Josep Lumbreras, Mikhail Terekhov, Marco Tomamichel
We initiate the study of sample-optimal quantum state tomography with minimal disturbance to the samples. Can we efficiently learn a precise description of a quantum state through…
Linear bandits with polylogarithmic minimax regret
Josep Lumbreras, Marco Tomamichel
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 un…
Learning finitely correlated states: stability of the spectral reconstruction
Marco Fanizza, Niklas Galke, Josep Lumbreras +2
Matrix product operators allow efficient descriptions (or realizations) of states on a 1D lattice. We consider the task of learning a realization of minimal dimension from copies o…