The Levy-Lieb embedding of density functional theory and its Quantum
Kernel: Illustration for the Hubbard Dimer using near-term quantum algorithms
- URL: http://arxiv.org/abs/2207.08995v1
- Date: Tue, 19 Jul 2022 00:23:52 GMT
- Title: The Levy-Lieb embedding of density functional theory and its Quantum
Kernel: Illustration for the Hubbard Dimer using near-term quantum algorithms
- Authors: C. D. Pemmaraju, Amol Deshmukh
- Abstract summary: We numerically implement the Levy-Lieb procedure for a paradigmatic lattice system, the Hubbard dimer.
We demonstrate density variational minimization using the resulting hybrid quantum-classical scheme.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The constrained-search formulation of Levy and Lieb provides a concrete
mapping from N-representable densities to the space of N-particle wavefunctions
and explicitly defines the universal functional of density functional theory.
We numerically implement the Levy-Lieb procedure for a paradigmatic lattice
system, the Hubbard dimer, using a modified variational quantum eigensolver
approach. We demonstrate density variational minimization using the resulting
hybrid quantum-classical scheme featuring real-time computation of the
Levy-Lieb functional along the search trajectory. We further illustrate a
fidelity based quantum kernel associated with the density to pure-state
embedding implied by the Levy-Lieb procedure and employ the kernel for learning
observable functionals of the density. We study the kernel's ability to
generalize with high accuracy through numerical experiments on the Hubbard
dimer.
Related papers
- Kernel Density Machines [0.0]
kernel density machines (KDM) are a novel density ratio estimator in a reproducing kernel Hilbert space setting.
We provide theoretical guarantees, including consistency, a functional central limit theorem, and finite-sample error bounds.
Empirical results based on simulated and real data demonstrate the efficacy and precision of KDM.
arXiv Detail & Related papers (2025-04-30T08:25:25Z) - Predicting fermionic densities using a Projected Quantum Kernel method [0.0]
We use a support vector regressor based on a projected quantum kernel method to predict the density structure of 1D fermionic systems.
The kernel is built on with the observables of a quantum reservoir implementable with interacting Rydberg atoms.
arXiv Detail & Related papers (2025-04-18T18:00:03Z) - Discretization of Structured Bosonic Environments at Finite Temperature by Interpolative Decomposition: Theory and Application [0.0]
We present a novel method to discretize the spectral density of a bosonic heat bath.
By capturing the time, frequency, and temperature dependencies encoded in the spectral density-autocorrelation function relation, our method significantly reduces the degrees of freedom required for open quantum system dynamics.
arXiv Detail & Related papers (2024-12-18T12:38:10Z) - Highly Accurate Real-space Electron Densities with Neural Networks [7.176850154835262]
We introduce a novel method to obtain accurate densities from real-space many-electron wave functions.
We use variational quantum Monte Carlo with deep-learning ans"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors.
arXiv Detail & Related papers (2024-09-02T14:56:22Z) - Multi-qubit quantum state preparation enabled by topology optimization [0.0]
We inverse-design nanophotonic cavities enabling the preparation of pure states of pairs and triples of quantum emitters.
Our findings open the way towards the efficient and fast preparation of multiqubit quantum states with engineered features.
arXiv Detail & Related papers (2024-05-24T08:52:22Z) - Expressibility-induced Concentration of Quantum Neural Tangent Kernels [4.561685127984694]
We study the connections between the trainability and expressibility of quantum tangent kernel models.
For global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero.
Our discoveries unveil a pivotal characteristic of quantum neural tangent kernels, offering valuable insights for the design of wide quantum variational circuit models.
arXiv Detail & Related papers (2023-11-08T19:00:01Z) - D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT)
We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity.
In addition, we show that our approach enables us to explore more complex neural-based wave functions.
arXiv Detail & Related papers (2023-03-01T10:38:10Z) - Density-potential inversion from Moreau-Yosida regularization [0.0]
Zhao-Morrison-Parr method is used to compute the effective potential that yields precisely that density.
We show how this and similar inversion procedures relate to the Moreau-Yosida regularization of density functionals on Banach spaces.
arXiv Detail & Related papers (2022-12-24T12:40:13Z) - Dilute neutron star matter from neural-network quantum states [58.720142291102135]
Low-density neutron matter is characterized by the formation of Cooper pairs and the onset of superfluidity.
We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-network quantum states combined with variational Monte Carlo and reconfiguration techniques.
arXiv Detail & Related papers (2022-12-08T17:55:25Z) - Calculating non-linear response functions for multi-dimensional
electronic spectroscopy using dyadic non-Markovian quantum state diffusion [68.8204255655161]
We present a methodology for simulating multi-dimensional electronic spectra of molecular aggregates with coupling electronic excitation to a structured environment.
A crucial aspect of our approach is that we propagate the NMQSD equation in a doubled system Hilbert space but with the same noise.
arXiv Detail & Related papers (2022-07-06T15:30:38Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Density-Based Clustering with Kernel Diffusion [59.4179549482505]
A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms.
We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
arXiv Detail & Related papers (2021-10-11T09:00:33Z) - Machine Learning Universal Bosonic Functionals [0.0]
A functional theory for bosonic ground states establishes the existence of a universal functional $mathcalF[gamma]$ that recovers quantum correlations exactly.
For the Bose-Hubbard model, we present a comparison between our approach and Quantum Monte Carlo.
arXiv Detail & Related papers (2021-04-07T15:53:10Z) - Method of spectral Green functions in driven open quantum dynamics [77.34726150561087]
A novel method based on spectral Green functions is presented for the simulation of driven open quantum dynamics.
The formalism shows remarkable analogies to the use of Green functions in quantum field theory.
The method dramatically reduces computational cost compared with simulations based on solving the full master equation.
arXiv Detail & Related papers (2020-06-04T09:41:08Z)
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.