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…