228 citations · 1.5k across the 45 of their papers we have counts for
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Diabatic Quantum Annealing for the Frustrated Ring Model
Jeremy Côté, Frédéric Sauvage, Martín Larocca +3
Quantum annealing is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolat…
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Louis Schatzki, Martin Larocca, Quynh T. Nguyen +2
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on qu…
Theory for Equivariant Quantum Neural Networks
Quynh T. Nguyen, Louis Schatzki, Paolo Braccia +5
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakth…
Representation Theory for Geometric Quantum Machine Learning
Michael Ragone, Paolo Braccia, Quynh T. Nguyen +5
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importatio…
Building spatial symmetries into parameterized quantum circuits for faster training
Frederic Sauvage, Martin Larocca, Patrick J. Coles +1
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates emplo…
Group-Invariant Quantum Machine Learning
Martin Larocca, Frederic Sauvage, Faris M. Sbahi +3
Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., w…