Mode-resolved thermometry of trapped ion with Deep Learning
- URL: http://arxiv.org/abs/2402.19022v1
- Date: Thu, 29 Feb 2024 10:33:04 GMT
- Title: Mode-resolved thermometry of trapped ion with Deep Learning
- Authors: Yi Tao, Ting Chen, Yi Xie, Hongyang Wang, Jie Zhang, Ting Zhang,
Pingxing Chen, Wei Wu
- Abstract summary: In trapped ion system, accurate thermometry of ion is crucial for evaluating the system state and performing quantum operations.
In this work, we apply deep learning for the first time to the thermometry of trapped ion.
Our trained neural network model can be directly applied to other experimental setups without retraining or post-processing.
- Score: 15.875697446765207
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In trapped ion system, accurate thermometry of ion is crucial for evaluating
the system state and precisely performing quantum operations. However, when the
motional state of a single ion is far away from the ground state, the spatial
dimension of the phonon state sharply increases, making it difficult to realize
accurate and mode-resolved thermometry with existing methods. In this work, we
apply deep learning for the first time to the thermometry of trapped ion,
providing an efficient and mode-resolved method for accurately estimating large
mean phonon numbers. Our trained neural network model can be directly applied
to other experimental setups without retraining or post-processing, as long as
the related parameters are covered by the model's effective range, and it can
also be conveniently extended to other parameter ranges. We have conducted
experimental verification based on our surface trap, of which the result has
shown the accuracy and efficiency of the method for thermometry of single ion
under large mean phonon number, and its mode resolution characteristic can make
it better applied to the characterization of system parameters, such as
evaluating cooling effectiveness, analyzing surface trap noise.
Related papers
- Thermometry of Trapped Ions Based on Bichromatic Driving [10.452541695685712]
A thermometry method based on bichromatic driving was theoretically proposed by Ivan Vybornyi et al.
We provide a detailed statistical analysis of this method and prove its robustness to several imperfect experimental conditions.
Our theoretical analysis and experimental verification demonstrate that the scheme can accurately and efficiently measure the temperature in ion crystals.
arXiv Detail & Related papers (2024-07-21T14:46:57Z) - Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation
for high-dimensional problems: uncertainty quantification and inverse
modeling [62.997667081978825]
We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems.
We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response.
arXiv Detail & Related papers (2023-07-05T18:14:38Z) - Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
We propose a Monte Carlo PDE solver for training unsupervised neural solvers.
We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.
Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
arXiv Detail & Related papers (2023-02-10T08:05:19Z) - Energy measurements remain thermometrically optimal beyond weak coupling [0.0]
We develop a general perturbative theory of finite-coupling quantum thermometry up to second order in probe-sample interaction.
By assumption, the probe and sample are in thermal equilibrium, so the probe is described by the mean-force Gibbs state.
We prove that the ultimate thermometric precision can be achieved - to second order in the coupling.
arXiv Detail & Related papers (2023-02-06T19:01:07Z) - Optimal cold atom thermometry using adaptive Bayesian strategies [0.0]
We propose an adaptive Bayesian framework that substantially boosts the performance of cold atom temperature estimation.
Unlike conventional methods, our proposal systematically avoids capturing and processing uninformative data.
We are able to produce much more reliable estimates, especially when the measured data are scarce and noisy.
arXiv Detail & Related papers (2022-04-25T17:48:03Z) - Bosonic field digitization for quantum computers [62.997667081978825]
We address the representation of lattice bosonic fields in a discretized field amplitude basis.
We develop methods to predict error scaling and present efficient qubit implementation strategies.
arXiv Detail & Related papers (2021-08-24T15:30:04Z) - Role of topology in determining the precision of a finite thermometer [58.720142291102135]
We find that low connectivity is a resource to build precise thermometers working at low temperatures.
We compare the precision achievable by position measurement to the optimal one, which itself corresponds to energy measurement.
arXiv Detail & Related papers (2021-04-21T17:19:42Z) - Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
Inferring the parameters of a model based on experimental observations is central to the scientific method.
A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations.
We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
arXiv Detail & Related papers (2021-02-12T12:23:13Z) - Adiabatic Sensing Technique for Optimal Temperature Estimation using
Trapped Ions [64.31011847952006]
We propose an adiabatic method for optimal phonon temperature estimation using trapped ions.
The relevant information of the phonon thermal distributions can be transferred to the collective spin-degree of freedom.
We show that each of the thermal state probabilities is adiabatically mapped onto the respective collective spin-excitation configuration.
arXiv Detail & Related papers (2020-12-16T12:58:08Z) - Optimal Quantum Thermometry with Coarse-grained Measurements [0.0]
We explore the precision limits for temperature estimation when only coarse-grained measurements are available.
We apply our results to many-body systems and nonequilibrium thermometry.
arXiv Detail & Related papers (2020-11-20T17:12:55Z)
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.