15 citations · 19 across the 8 of their papers we have counts for
9 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.…
Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
Javier Mancilla, André Sequeira, Tomas Tagliani +2
Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing inte…
On Quantum Natural Policy Gradients
André Sequeira, Luis Paulo Santos, Luis Soares Barbosa
This research delves into the role of the quantum Fisher Information Matrix (FIM) in enhancing the performance of Parameterized Quantum Circuit (PQC)-based reinforcement learning a…
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
Rodrigo Coelho, André Sequeira, Luís 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…
Policy Gradients using Variational Quantum Circuits
André Sequeira, Luis Paulo Santos, Luís Soares Barbosa
Variational Quantum Circuits are being used as versatile Quantum Machine Learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. H…
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…