149 citations · 282 across the 6 of their papers we have counts for
8 papers
On the Metastability of the Mean-Field Interchange Model for Local Dimension
Sergio Escobar, Lin Lin, Michael Ragone +1
Many quantum systems are believed to thermalize slowly at low temperatures due to the existence of metastable phases. However, there are cases where further cooling can restore pol…
Quantum Markov State Models for Metastable Dynamics
Hao-En Li, Lin Lin, Michael Ragone
Open quantum systems can rapidly lose most microscopic information, leaving only a few degrees of freedom to govern their long-time dynamics. Classical Markov state models (MSMs) d…
Spectral Gap of the Davies Generator for the Mean-Field Heisenberg Model
Joao Basso, Thiago Bergamaschi, Lin Lin +2
The mean-field Heisenberg ferromagnet is a quantum spin model on the complete graph with isotropic spin-1/2 interactions. This non-commuting Hamiltonian is permutation and $\mathsf…
The Many-Body Ground State Manifold of Flat Band Interacting Hamiltonian for Magic Angle Twisted Bilayer Graphene
Kevin D. Stubbs, Michael Ragone, Allan H. MacDonald +1
At a magic relative twist angle, magic angle twisted bilayer graphene (MATBG) has an octet of flat bands that can host strong correlation physics when partially filled. A key theor…
SO(n) AKLT Chains as Symmetry Protected Topological Quantum Ground States
Michael Ragone
This thesis studies a pair of symmetry protected topological (SPT) phases which arise when considering one-dimensional quantum spin systems possessing a natural orthogonal group sy…
A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
Michael Ragone, Bojko N. Bakalov, Frédéric Sauvage +4
Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator…
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…