4 papers · 1 filter
Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
Gilberto Cunha, Alexandra Ramôa, André Sequeira +2
Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactl…
A hybrid classical-quantum approach to highly constrained Unit Commitment problems
Bruna Salgado, André Sequeira, Luis Paulo Santos
The unit commitment (UC) problem stands as a critical optimization challenge in the electrical power industry. It is classified as NP-hard, placing it among the most intractable pr…
Trainability issues in quantum policy gradients
André Sequeira, Luis Paulo Santos, Luis Soares Barbosa
This research explores the trainability of Parameterized Quantum circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration.…
Generalised Quantum Tree Search
Andre Sequeira, Luis Paulo Santos, Luis Soares Barbosa
This extended abstract reports on on-going research on quantum algorithmic approaches to the problem of generalised tree search that may exhibit effective quantum speedup, even in…