Quantum correlation functions through tensor network path integral
- URL: http://arxiv.org/abs/2308.10540v2
- Date: Wed, 30 Aug 2023 08:16:02 GMT
- Title: Quantum correlation functions through tensor network path integral
- Authors: Amartya Bose
- Abstract summary: tensor networks are utilized for calculating equilibrium correlation function for open quantum systems.
The influence of the solvent on the quantum system is incorporated through an influence functional.
The design and implementation of this method is discussed along with illustrations from rate theory, symmetrized spin correlation functions, dynamical susceptibility calculations and quantum thermodynamics.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tensor networks have historically proven to be of great utility in providing
compressed representations of wave functions that can be used for calculation
of eigenstates. Recently, it has been shown that a variety of these networks
can be leveraged to make real time non-equilibrium simulations of dynamics
involving the Feynman-Vernon influence functional more efficient. In this work,
tensor networks are utilized for calculating equilibrium correlation function
for open quantum systems using the path integral methodology. These correlation
functions are of fundamental importance in calculations of rates of reactions,
simulations of response functions and susceptibilities, spectra of systems,
etc. The influence of the solvent on the quantum system is incorporated through
an influence functional, whose unconventional structure motivates the design of
a new optimal matrix product-like operator that can be applied to the so-called
path amplitude matrix product state. This complex time tensor network path
integral approach provides an exceptionally efficient representation of the
path integral enabling simulations for larger systems strongly interacting with
baths and at lower temperatures out to longer time. The design and
implementation of this method is discussed along with illustrations from rate
theory, symmetrized spin correlation functions, dynamical susceptibility
calculations and quantum thermodynamics.
Related papers
- Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Capturing long-range memory structures with tree-geometry process tensors [0.0]
We introduce a class of quantum non-Markovian processes that exhibit decaying temporal correlations and memory distributed across time scales.
We show that the long-range correlations in this class of processes tends to originate almost entirely from memory effects.
We show how it can efficiently approximate the strong memory dynamics of the paradigm spin-boson model.
arXiv Detail & Related papers (2023-12-07T19:00:01Z) - A linear response framework for simulating bosonic and fermionic
correlation functions illustrated on quantum computers [58.720142291102135]
Lehmann formalism for obtaining response functions in linear response has no direct link to experiment.
Within the context of quantum computing, we make the experiment an inextricable part of the quantum simulation.
We show that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material.
arXiv Detail & Related papers (2023-02-20T19:01:02Z) - Quantum circuits for the preparation of spin eigenfunctions on quantum
computers [63.52264764099532]
Hamiltonian symmetries are an important instrument to classify relevant many-particle wavefunctions.
This work presents quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers.
arXiv Detail & Related papers (2022-02-19T00:21:46Z) - Decimation technique for open quantum systems: a case study with
driven-dissipative bosonic chains [62.997667081978825]
Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics.
We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function.
We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
arXiv Detail & Related papers (2022-02-15T19:00:09Z) - Collisional open quantum dynamics with a generally correlated
environment: Exact solvability in tensor networks [0.0]
We find a natural Markovian embedding for the system dynamics, where the role of an auxiliary system is played by virtual indices of the network.
The results advance tensor-network methods in the fields of quantum optics and quantum transport.
arXiv Detail & Related papers (2022-02-09T19:48:17Z) - A tensor network representation of path integrals: Implementation and
analysis [0.0]
We introduce a novel tensor network-based decomposition of path integral simulations involving Feynman-Vernon influence functional.
The finite temporarily non-local interactions introduced by the influence functional can be captured very efficiently using matrix product state representation.
The flexibility of the AP-TNPI framework makes it a promising new addition to the family of path integral methods for non-equilibrium quantum dynamics.
arXiv Detail & Related papers (2021-06-23T16:41:54Z) - Constructing Tensor Network Influence Functionals for General Quantum
Dynamics [0.0]
We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of the bond dimensions and time-like entanglement.
We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes.
As one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.
arXiv Detail & Related papers (2021-01-14T05:42:25Z) - Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with
an artificial neural-network correlator ansatz [62.997667081978825]
We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei.
We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
arXiv Detail & Related papers (2020-07-28T14:52:28Z) - UNIPoint: Universally Approximating Point Processes Intensities [125.08205865536577]
We provide a proof that a class of learnable functions can universally approximate any valid intensity function.
We implement UNIPoint, a novel neural point process model, using recurrent neural networks to parameterise sums of basis function upon each event.
arXiv Detail & Related papers (2020-07-28T09:31:56Z)
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.