Approximating quantum thermodynamic properties using DFT
- URL: http://arxiv.org/abs/2201.05563v2
- Date: Mon, 25 Apr 2022 12:54:09 GMT
- Title: Approximating quantum thermodynamic properties using DFT
- Authors: Krissia Zawadzki, Amy Skelt and Irene D'Amico
- Abstract summary: We compare simple' and hybrid' approximations to the average work and entropy variation built on static density functional theory concepts.
Our results confirm that a hybrid' approach requires a very good approximation of the initial and, for the entropy, final states of the system.
This approach should be particularly efficient when many-body effects are not increased by the driving Hamiltonian.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The fabrication, utilisation, and efficiency of quantum technologies rely on
a good understanding of quantum thermodynamic properties. Many-body systems are
often used as hardware for these quantum devices, but interactions between
particles make the complexity of related calculations grow exponentially with
the system size. Here we explore and systematically compare `simple' and
`hybrid' approximations to the average work and entropy variation built on
static density functional theory concepts. These approximations are
computationally cheap and could be applied to large systems. We exemplify them
considering driven one-dimensional Hubbard chains and show that, for `simple'
approximations and low to medium temperatures, it pays to consider a good
Kohn-Sham Hamiltonian to approximate the driving Hamiltonian. Our results
confirm that a `hybrid' approach, requiring a very good approximation of the
initial and, for the entropy, final states of the system, provides great
improvements. This approach should be particularly efficient when many-body
effects are not increased by the driving Hamiltonian.
Related papers
- Optimal quantum algorithm for Gibbs state preparation [2.403252956256118]
A recently introduced disispative evolution has been shown to be efficiently implementable on a quantum computer.
We prove that, at high enough temperatures, this evolution reaches the Gibbs state in time scaling logarithmically with system size.
We then employ our result to the problem of estimating partition functions at high temperature, showing an improved performance over previous classical and quantum algorithms.
arXiv Detail & Related papers (2024-11-07T17:21:26Z) - Optimizing random local Hamiltonians by dissipation [44.99833362998488]
We prove that a simplified quantum Gibbs sampling algorithm achieves a $Omega(frac1k)$-fraction approximation of the optimum.
Our results suggest that finding low-energy states for sparsified (quasi)local spin and fermionic models is quantumly easy but classically nontrivial.
arXiv Detail & Related papers (2024-11-04T20:21:16Z) - High-efficiency quantum Monte Carlo algorithm for extracting entanglement entropy in interacting fermion systems [4.758738320755899]
We propose a fermionic quantum Monte Carlo algorithm based on the incremental technique along physical parameters.
We show the effectiveness of the algorithm and show the high precision.
In this simulation, the calculated scaling behavior of the entanglement entropy elucidates the different phases of the Fermi surface and Goldstone modes.
arXiv Detail & Related papers (2024-09-30T07:07:51Z) - Efficiency of Dynamical Decoupling for (Almost) Any Spin-Boson Model [44.99833362998488]
We analytically study the dynamical decoupling of a two-level system coupled with a structured bosonic environment.
We find sufficient conditions under which dynamical decoupling works for such systems.
Our bounds reproduce the correct scaling in various relevant system parameters.
arXiv Detail & Related papers (2024-09-24T04:58:28Z) - 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) - Third quantization with Hartree approximation for open-system bosonic transport [49.1574468325115]
We present a self-consistent formalism for solving the open-system bosonic Lindblad equation with weak interactions in the steady state.
The method allows us to characterize and predict large-system behavior of quantum transport in interacting bosonic systems relevant to cold-atom experiments.
arXiv Detail & Related papers (2024-08-23T15:50:48Z) - Efficient thermalization and universal quantum computing with quantum Gibbs samplers [2.403252956256118]
We show adiabatic preparation of the associated "thermofield double" states.
We show implementing this family of dissipative evolutions for inverse temperatures in the system's size is computationally equivalent to standard quantum computations.
Taken together, our results show that a family of quasi-local dissipative evolution efficiently prepares a large class of quantum many-body states.
arXiv Detail & Related papers (2024-03-19T12:49:25Z) - Capturing many-body correlation effects with quantum and classical
computing [40.7853309684189]
We show the efficiency of Quantum Phase Estor (QPE) in identifying core-level states relevant to x-ray photoelectron spectroscopy.
We compare and validate the QPE predictions with exact diagonalization and real-time equation-of-motion coupled cluster formulations.
arXiv Detail & Related papers (2024-02-18T01:26:45Z) - An integral algorithm of exponential observables for interacting fermions in quantum Monte Carlo simulation [7.826326818086168]
Exponential observables, formulated as $log langle ehatXrangle$ where $hatX$ is an extensive quantity, play a critical role in study of quantum many-body systems.
We propose a comprehensive algorithm for quantifying these observables in interacting fermion systems.
arXiv Detail & Related papers (2023-11-06T19:00:04Z) - Circuit quantum electrodynamics (cQED) with modular quasi-lumped models [0.23624125155742057]
Method partitions a quantum device into compact lumped or quasi-distributed cells.
We experimentally validate the method on large-scale, state-of-the-art superconducting quantum processors.
arXiv Detail & Related papers (2021-03-18T16:03:37Z) - 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)
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.