205 citations · 467 across the 14 of their papers we have counts for
13 papers
Computable fermionic non-Gaussianity from the covariance matrix
Poetri Sonya Tarabunga, Bernhard Jobst, Raúl Morral-Yepes +4
Fermionic non-Gaussianity, or fermionic magic, is a key resource underlying the computational complexity of fermionic quantum systems, yet tractable and operationally meaningful wa…
Backpropagating Pauli Propagation
Sheng-Hsuan Lin, Etienne Granet, Kevin Hémery +1
We develop a backpropagation algorithm for evaluating parameter gradients in quantum circuits using Pauli propagation simulation. The method has computational complexity comparable…
Quantum trajectory simulation of two-dimensional non-equilibrium steady states with a trapped ion quantum processor
Anna Dalmasso, Arash Jafarizadeh, Julian Boesl +8
Digital quantum computers offer a promising route for studying complex many-body systems that are otherwise inaccessible by their classical counterparts. Capabilities including mid…
Digital quantum magnetism on a trapped-ion quantum computer
Reza Haghshenas, Eli Chertkov, Michael Mills +56
Digital quantum matter -- realized when discrete quantum gates approximate continuous time evolution -- is susceptible to heating into chaotic, structureless states. If digitizatio…
Alternating and Gaussian fermionic Isometric Tensor Network States
Yantao Wu, Zhehao Dai, Sajant Anand +5
Isometric tensor networks in two dimensions enable efficient and accurate study of quantum many-body states, yet the effect of the isometric restriction on the represented quantum…
Distributive Pre-Training of Generative Modeling Using Matrix-Product States
Sheng-Hsuan Lin, Olivier Kuijpers, Sebastian Peterhansl +1
Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models ar…
Two Dimensional Isometric Tensor Networks on an Infinite Strip
Yantao Wu, Sajant Anand, Sheng-Hsuan Lin +2
The exact contraction of a generic two-dimensional (2D) tensor network state (TNS) is known to be exponentially hard, making simulation of 2D systems difficult. The recently introd…
Time Evolution of Uniform Sequential Circuits
Nikita Astrakhantsev, Sheng-Hsuan Lin, Frank Pollmann +1
Simulating time evolution of generic quantum many-body systems using classical numerical approaches has an exponentially growing cost either with evolution time or with the system…
Efficient Simulation of Dynamics in Two-Dimensional Quantum Spin Systems with Isometric Tensor Networks
Sheng-Hsuan Lin, Michael Zaletel, Frank Pollmann
We investigate the computational power of the recently introduced class of isometric tensor network states (isoTNSs), which generalizes the isometric conditions of the canonical fo…
Scaling of neural-network quantum states for time evolution
Sheng-Hsuan Lin, Frank Pollmann
Simulating quantum many-body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space. Artificial neural networks have recently b…
Real- and imaginary-time evolution with compressed quantum circuits
Sheng-Hsuan Lin, Rohit Dilip, Andrew G. Green +2
The current generation of noisy intermediate scale quantum computers introduces new opportunities to study quantum many-body systems. In this paper, we show that quantum circuits c…
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Giuseppe Carleo, Kenny Choo, Damian Hofmann +15
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and…
Many-body localization of spinless fermions with attractive interactions in one dimension
Sheng-Hsuan Lin, B. Sbierski, F. Dorfner +2
We study the finite-energy density phase diagram of spinless fermions with attractive interactions in one dimension in the presence of uncorrelated diagonal disorder. Unlike the ca…