Neural network enhanced measurement efficiency for molecular
groundstates
- URL: http://arxiv.org/abs/2206.15449v2
- Date: Mon, 12 Sep 2022 14:24:45 GMT
- Title: Neural network enhanced measurement efficiency for molecular
groundstates
- Authors: Dmitri Iouchtchenko, J\'er\^ome F. Gonthier, Alejandro Perdomo-Ortiz,
Roger G. Melko
- Abstract summary: 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.
- Score: 63.36515347329037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is believed that one of the first useful applications for a quantum
computer will be the preparation of groundstates of molecular Hamiltonians. A
crucial task involving state preparation and readout is obtaining physical
observables of such states, which are typically estimated using projective
measurements on the qubits. At present, measurement data is costly and
time-consuming to obtain on any quantum computing architecture, which has
significant consequences for the statistical errors of estimators. In this
paper, we adapt common neural network models (restricted Boltzmann machines and
recurrent neural networks) to learn complex groundstate wavefunctions for
several prototypical molecular qubit Hamiltonians from typical measurement
data. By relating the accuracy $\varepsilon$ of the reconstructed groundstate
energy to the number of measurements, we find that using a neural network model
provides a robust improvement over using single-copy measurement outcomes alone
to reconstruct observables. This enhancement yields an asymptotic scaling near
$\varepsilon^{-1}$ for the model-based approaches, as opposed to
$\varepsilon^{-2}$ in the case of classical shadow tomography.
Related papers
- Neural network enhanced cross entropy benchmark for monitored circuits [0.0]
We use a recurrent neural network to learn a representation of the measurement record for a native trapped-ion MIPT.
We show that using this generative model can substantially reduce the number of measurements required to accurately estimate the cross entropy.
arXiv Detail & Related papers (2025-01-22T16:46:39Z) - Data Efficient Prediction of excited-state properties using Quantum Neural Networks [4.7436936193373604]
We present a quantum machine learning model that predicts excited-state properties from the molecular ground state.
The proposed procedure is fully NISQ compatible.
We show that the procedure is able to outperform various classical models that rely solely on classical features.
arXiv Detail & Related papers (2024-12-12T16:30:23Z) - 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) - Quantum states from normalizing flows [0.0]
We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows.
We demonstrate the use of this architecture for both ground-state preparation and real-time evolution.
arXiv Detail & Related papers (2024-06-04T16:16:58Z) - Neural network approach to quasiparticle dispersions in doped
antiferromagnets [0.0]
We study the ability of neural quantum states to represent the bosonic and fermionic $t-J$ model on different 1D and 2D lattices.
We present a method to calculate dispersion relations from the neural network state representation.
arXiv Detail & Related papers (2023-10-12T17:59:33Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Efficient quantum state tomography with convolutional neural networks [0.0]
We develop a quantum state tomography scheme which relies on approxing the probability distribution over the outcomes of an informationally complete measurement.
It achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.
arXiv Detail & Related papers (2021-09-28T14:55:54Z) - Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators.
We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states.
We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
arXiv Detail & Related papers (2020-07-31T17:25:12Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
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.