3 papers
quant-ph2026
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…
quant-ph2024
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…
quant-ph2024
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
Rodrigo Coelho, André Sequeira, LuÃs Paulo Santos
Reinforcement Learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networ…