8 papers
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…
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…
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…