5 papers
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…
A Laplacian-based Quantum Graph Neural Network for Semi-Supervised Learning
Hamed Gholipour, Farid Bozorgnia, Kailash Hambarde +5
Laplacian learning method is a well-established technique in classical graph-based semi-supervised learning, but its potential in the quantum domain remains largely unexplored. Thi…
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…