24 citations · 55 across the 12 of their papers we have counts for
14 papers
Sampling two-dimensional isometric tensor network states
Alec Dektor, Eugene Dumitrescu, Chao Yang
Sampling a quantum system's underlying probability distributions is an important computational task, e.g., for quantum advantage experiments and quantum Monte Carlo algorithms. Ten…
Computing excited states with isometric tensor networks in two-dimensions
Alec Dektor, Runze Chi, Roel Van Beeumen +1
We present a new subspace iteration method for computing low-lying eigenpairs (excited states) of high-dimensional quantum many-body Hamiltonians with nearest neighbor interactions…
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
Hardeep Bassi, Yuanran Zhu, Erika Ye +5
We present RELift (Restrict, Evolve, Lift), a two-phase learning framework that couples coarse-grid numerical solvers with neural operators to super-resolve and forecast fine-grid…
Numerical Optimization for Tensor Disentanglement
Julia Wei, Alec Dektor, Chungen Shen +2
Tensor networks provide compact and scalable representations of high-dimensional data, enabling efficient computation in fields such as quantum physics, numerical partial different…
Inexact subspace projection methods for low-rank tensor eigenvalue problems
Alec Dektor, Peter DelMastro, Erika Ye +2
We propose inexact subspace iteration for solving high-dimensional eigenvalue problems with low-rank structure. Inexactness stems from low-rank compression, enabling efficient repr…
Interpolatory dynamical low-rank approximation for the 3+3d Boltzmann-BGK equation
Alec Dektor, Lukas Einkemmer
We introduce two novel interpolatory dynamical low-rank (DLR) approximation methods for the efficient time integration of the Boltzmann-BGK equation. Both methods overcome limitati…