Isometric tensor network representations of two-dimensional thermal
states
- URL: http://arxiv.org/abs/2302.07905v2
- Date: Mon, 8 May 2023 10:36:30 GMT
- Title: Isometric tensor network representations of two-dimensional thermal
states
- Authors: Wilhelm Kadow, Frank Pollmann, Michael Knap
- Abstract summary: We use the class of recently introduced tensor network states to represent thermal states of the transverse field Ising model.
We find that this approach offers a different way with low computational complexity to represent states at finite temperatures.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tensor networks provide a useful tool to describe low-dimensional complex
many-body systems. Finding efficient algorithms to use these methods for
finite-temperature simulations in two dimensions is a continuing challenge.
Here, we use the class of recently introduced isometric tensor network states,
which can also be directly realized with unitary gates on a quantum computer.
We utilize a purification ansatz to efficiently represent thermal states of the
transverse field Ising model. By performing an imaginary-time evolution
starting from infinite temperature, we find that this approach offers a
different way with low computational complexity to represent states at finite
temperatures.
Related papers
- Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering [0.0]
We show that temperature optimization can significantly increase the success rate of variational algorithms.
We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network.
arXiv Detail & Related papers (2024-10-30T13:48:35Z) - Augmenting Finite Temperature Tensor Network with Clifford Circuits [0.49157446832511503]
Recent studies have highlighted the combination of network methods and the stabilizer formalism as a very effective framework for simulating quantum many-body systems.
In this work, we adapt this paradigm for finite temperature simulations in the framework of Time-Dependent Variational Principle.
Our numerical results demonstrate that Clifford circuits can significantly improve the efficiency and accuracy of finite temperature simulations.
arXiv Detail & Related papers (2024-10-21T07:30:59Z) - High-precision simulation of finite-size thermalizing systems at long times [6.907555940790131]
We propose a simple and efficient numerical method so that the simulation error is of higher order in $1/N$.
This finite-size error scaling is proved by assuming the eigenstate thermalization hypothesis.
arXiv Detail & Related papers (2024-06-08T08:39:21Z) - Real-time quantum dynamics of thermal states with neural thermofields [0.0]
We study the real-time dynamics of thermal states in two dimensions, based on thermofield dynamics, variational Monte Carlo, and neural-network quantum states.
We provide predictions of the real-time dynamics on a 6x6 lattice that lies outside the reach of exact simulations.
arXiv Detail & Related papers (2023-09-13T16:23:28Z) - Robust Extraction of Thermal Observables from State Sampling and
Real-Time Dynamics on Quantum Computers [49.1574468325115]
We introduce a technique that imposes constraints on the density of states, most notably its non-negativity, and show that this way, we can reliably extract Boltzmann weights from noisy time series.
Our work enables the implementation of the time-series algorithm on present-day quantum computers to study finite temperature properties of many-body quantum systems.
arXiv Detail & Related papers (2023-05-30T18:00:05Z) - Adaptive variational quantum minimally entangled typical thermal states
for finite temperature simulations [0.0]
We describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm.
The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits.
arXiv Detail & Related papers (2023-01-06T16:40:06Z) - Neural network enhanced measurement efficiency for molecular
groundstates [63.36515347329037]
We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians.
We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
arXiv Detail & Related papers (2022-06-30T17:45:05Z) - 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) - Machine learning for rapid discovery of laminar flow channel wall
modifications that enhance heat transfer [56.34005280792013]
We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number.
We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
arXiv Detail & Related papers (2021-01-19T16:14:02Z) - Efficient construction of tensor-network representations of many-body
Gaussian states [59.94347858883343]
We present a procedure to construct tensor-network representations of many-body Gaussian states efficiently and with a controllable error.
These states include the ground and thermal states of bosonic and fermionic quadratic Hamiltonians, which are essential in the study of quantum many-body systems.
arXiv Detail & Related papers (2020-08-12T11:30:23Z) - Simulation of Thermal Relaxation in Spin Chemistry Systems on a Quantum
Computer Using Inherent Qubit Decoherence [53.20999552522241]
We seek to take advantage of qubit decoherence as a resource in simulating the behavior of real world quantum systems.
We present three methods for implementing the thermal relaxation.
We find excellent agreement between our results, experimental data, and the theoretical prediction.
arXiv Detail & Related papers (2020-01-03T11:48:11Z)
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.