11 papers
Compressing multivariate functions with tree tensor networks
Joseph Tindall, E. Miles Stoudenmire, Ryan Levy
Tensor networks are a compressed format for multi-dimensional data. One dimensional tensor networks -- often referred to as tensor trains (TT) or matrix product states (MPS) -- are…
Fast Tensor Network Imaginary Time Evolution by Implicit Stepping on Logarithmic Grids
John P. Zima, E. Miles Stoudenmire, Steven R. White +2
We present a new method for the efficient imaginary time evolution of quantum many-body wavefunctions represented by matrix product states (MPS). We first show that logarithmic tim…
Finite-temperature formation of magnetic plateaus and simplex liquid states on the frustrated ruby lattice
Antonio Francesco Mello, E. Miles Stoudenmire, Joseph Tindall
Geometric frustration in quantum systems can stabilize unconventional phases of matter that avoid traditional magnetic ordering at low temperatures. Here, we observe this phenomeno…
Recursive Sketched Interpolation: Efficient Hadamard Products of Tensor Trains
Zhaonan Meng, Yuehaw Khoo, Jiajia Li +1
The Hadamard product of two tensors in the tensor-train (TT) format is a fundamental operation across various applications, such as TT-based function multiplication for nonlinear d…
Generative Modeling via Hierarchical Tensor Sketching
Yifan Peng, Yian Chen, E. Miles Stoudenmire +1
We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution. This leverages randomized singular value decomp…
Investigating a Quantum-Inspired Method for Quantum Dynamics
Bo Xiao, Benedikt Kloss, E. Miles Stoudenmire
Building on recent advances in quantum algorithms which measure and reuse qubits and in efficient classical simulation leveraging projective measurements, we extend these framework…