Random insights into the complexity of two-dimensional tensor network
calculations
- URL: http://arxiv.org/abs/2307.11053v1
- Date: Thu, 20 Jul 2023 17:34:06 GMT
- Title: Random insights into the complexity of two-dimensional tensor network
calculations
- Authors: Sofia Gonzalez-Garcia, Shengqi Sang, Timothy H. Hsieh, Sergio Boixo,
Guifre Vidal, Andrew C. Potter and Romain Vasseur
- Abstract summary: Projected entangled pair states offer memory-efficient representations of some quantum many-body states.
Projected entangled pair states offer memory-efficient representations of some quantum many-body states that obey an entanglement area law.
- Score: 0.27708222692419743
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Projected entangled pair states (PEPS) offer memory-efficient representations
of some quantum many-body states that obey an entanglement area law, and are
the basis for classical simulations of ground states in two-dimensional (2d)
condensed matter systems. However, rigorous results show that exactly computing
observables from a 2d PEPS state is generically a computationally hard problem.
Yet approximation schemes for computing properties of 2d PEPS are regularly
used, and empirically seen to succeed, for a large subclass of (not too
entangled) condensed matter ground states. Adopting the philosophy of random
matrix theory, in this work we analyze the complexity of approximately
contracting a 2d random PEPS by exploiting an analytic mapping to an effective
replicated statistical mechanics model that permits a controlled analysis at
large bond dimension. Through this statistical-mechanics lens, we argue that:
i) although approximately sampling wave-function amplitudes of random PEPS
faces a computational-complexity phase transition above a critical bond
dimension, ii) one can generically efficiently estimate the norm and
correlation functions for any finite bond dimension. These results are
supported numerically for various bond-dimension regimes. It is an important
open question whether the above results for random PEPS apply more generally
also to PEPS representing physically relevant ground states
Related papers
- Projected Entangled Pair States with flexible geometry [0.0]
Projected Entangled Pair States (PEPS) are a class of quantum many-body states that generalize Matrix Product States for one-dimensional systems to higher dimensions.
PEPS have advanced understanding of strongly correlated systems, especially in two dimensions, e.g., quantum spin liquids.
We present a PEPS algorithm to simulate low-energy states and dynamics defined on arbitrary, fluctuating, and densely connected graphs.
arXiv Detail & Related papers (2024-07-30T19:03:52Z) - Thermalization and Criticality on an Analog-Digital Quantum Simulator [133.58336306417294]
We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution.
We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions.
We digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization.
arXiv Detail & Related papers (2024-05-27T17:40:39Z) - Dual-isometric Projected Entangled Pair States [0.29998889086656577]
We propose a new class of Project Entangled Pair State (PEPS) that incorporates two isometric conditions.
This new class facilitates the efficient calculation of general local observables and certain two-point correlation functions.
We analytically demonstrate that this class can encode universal quantum computations and can represent a transition from topological to trivial order.
arXiv Detail & Related papers (2024-04-25T17:31:31Z) - Efficient Representation of Minimally Entangled Typical Thermal States
in two dimensions via Projected Entangled Pair States [0.0]
The Minimally Entangled Typical Thermal States (METTS) are an ensemble of pure states, equivalent to the Gibbs thermal state, that can be efficiently represented by tensor networks.
In this article, we use the Projected Entangled Pair States (PEPS) ansatz as to represent METTS on a two-dimensional (2D) lattice.
Our analysis reveals that PEPS-METTS achieves accurate long-range correlations with significantly lower bond dimensions.
arXiv Detail & Related papers (2023-10-12T17:23:55Z) - Universality of critical dynamics with finite entanglement [68.8204255655161]
We study how low-energy dynamics of quantum systems near criticality are modified by finite entanglement.
Our result establishes the precise role played by entanglement in time-dependent critical phenomena.
arXiv Detail & Related papers (2023-01-23T19:23:54Z) - Finding the ground state of a lattice gauge theory with fermionic tensor
networks: a $2+1d$ $\mathbb{Z}_2$ demonstration [0.0]
We use Gauged Gaussian Fermionic PEPS to find the ground state of $2+1d$ dimensional pure $mathbbZ$ lattice gauge theories.
We do so by combining PEPS methods with Monte-Carlo computations, allowing for efficient contraction of the PEPS and computation of correlation functions.
arXiv Detail & Related papers (2022-10-31T18:00:02Z) - Regression of high dimensional angular momentum states of light [47.187609203210705]
We present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce.
We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through a quantum walk dynamics.
arXiv Detail & Related papers (2022-06-20T16:16:48Z) - Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency [105.17746223041954]
Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges.
It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon.
We propose a reinforcement learning algorithm named Embed to Control (ETC), which learns the representation at two levels while optimizing the policy.
arXiv Detail & Related papers (2022-05-26T16:34:46Z) - Neural-Network Quantum States for Periodic Systems in Continuous Space [66.03977113919439]
We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity.
For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles.
In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
arXiv Detail & Related papers (2021-12-22T15:27:30Z) - Efficient 2D Tensor Network Simulation of Quantum Systems [6.074275058563179]
2D tensor networks such as Projected Entangled States (PEPS) are well-suited for key classes of physical systems and quantum circuits.
We propose new algorithms and software abstractions for PEPS-based methods, accelerating the bottleneck operation of contraction and scalableization of a subnetwork.
arXiv Detail & Related papers (2020-06-26T22:36:56Z) - Gaussian Process States: A data-driven representation of quantum
many-body physics [59.7232780552418]
We present a novel, non-parametric form for compactly representing entangled many-body quantum states.
The state is found to be highly compact, systematically improvable and efficient to sample.
It is also proven to be a universal approximator' for quantum states, able to capture any entangled many-body state with increasing data set size.
arXiv Detail & Related papers (2020-02-27T15:54:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.