Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2103.02231 · Applied Physics Letters

Minimization of the micromotion of trapped ions with artificial neural networks

Yang Liu, Qifeng Lao, Pengfei Lu, Xinxin Rao, Hao Wu, Teng Liu, Kun-xu Wang, Zhao Wang, Ming-shen Li, Feng Zhu, Le Luo

Published Sep 27, 2021• 6 citations
arXivHTMLPDF

Abstract

Minimizing the micromotion of a trapped ion in a linear Paul trap is of great importance in maintaining long coherence time as well as implementing quantum logic gates with high fidelity, which is crucial for large-scale quantum computation with trapped ions. Here, by applying the RF (radio frequency)-photon correlation technique, we demonstrate that a machine learning method based on artificial neural networks can quickly search for optimal voltage settings of the electrodes to minimize the trapped ion's micromotion. This machine learning assisted RF-photon correlation technique can be straightforwardly applied to more complicated surface ion traps with many electrodes, where the manual minimization of the excess micromotion generated by stray electric fields would become extremely challenging for the larger number of electrodes with various voltage settings. Instead, the presented machine learning assisted method provides an effective and automatic way to address this need.

DOI
10.1063/5.0062508

Related papers

Ion-trajectory analysis for micromotion minimization and the measurement of small forces

Timm F. Gloger, Peter Kaufmann, Delia Kaufmann +4 more

For experiments with ions confined in a Paul trap, minimization of micromotion is often essential. In order to diagnose and compensate micromotion we have implemented a method that allows for finding the position of the radio-frequency (rf) null reliably and efficiently, in principle, without any variation of direct current (dc) voltages. We apply a trap modulation technique and focus-scanning imaging to extract three-dimensional ion positions for various rf drive powers and analyze the power dependence of the equilibrium position of the trapped ion. In contrast to commonly used methods, the search algorithm directly makes use of a physical effect as opposed to efficient numerical minimization in a high-dimensional parameter space. Using this method we achieve a compensation of the residual electric field that causes excess micromotion in the radial plane of a linear Paul trap down to $0.09\phantom{\rule{4pt}{0ex}}\text{V}\phantom{\rule{0.16em}{0ex}}{\text{m}}^{\ensuremath{-}1}$. Additionally, the precise position determination of a single harmonically trapped ion employed here can also be utilized for the detection of small forces. This is demonstrated by determining light pressure forces with a precision of 135 yN. As the method is based on imaging only, it can be applied to several ions simultaneously and is independent of laser direction and thus well suited to be used with, for example, surface-electrode traps.

arXiv:1503.07031•Physical Review A•Oct 27, 2015•39 citations

Micromotion minimisation by synchronous detection of parametrically excited motion

D. P. Nadlinger, P. Drmota, D. Main +6 more

Precise control of charged particles in radio-frequency (Paul) traps requires minimising excess micromotion induced by stray electric fields. We present a method to detect and compensate such fields through amplitude modulation of the radio-frequency trapping field. Modulation at frequencies close to the motional modes of the trapped particle excites coherent motion whose amplitude linearly depends on the stray field. In trapped-ion experiments, this motion can be detected by recording the arrival times of photons scattered during laser cooling. Only a single laser beam is required to resolve fields in multiple directions. In a demonstration using a $^{88}\mathrm{Sr}^{+}$ ion in a surface electrode trap, we achieve a sensitivity of $0.1\, \mathrm{V}\, \mathrm{m}^{-1}\, /\, \sqrt{\mathrm{Hz}}$ and a minimal uncertainty of $0.015\, \mathrm{V}\, \mathrm{m}^{-1}$.

arXiv:2107.00056•Jun 30, 2021•3 citations

Optimizing RF/DC Electrode Layouts in Ion Traps: Minimizing Micromotion Through Geometric Configuration for Enhanced Confinement and Laser Cooling Access

Kaz Bano

Ion traps are a foundational technology in quantum information processing, precision spectroscopy and foundational physics tests. At the heart of their performance is the inhibition of micromotion, an oscillation of ions through imperfect trapping potentials, that restricts the laser cooling efficiency, coherence times, and the accuracy of measurements. The paper provides a systematic computational analysis of four different electrode geometries (Linear Paul, Segmented, Surface and Ring) in 24 different RF / DC configurations (C01-C24) and 13 performance parameters that comprise trap depth (0.62 1.48 eV), radial secular frequency (1,100 1 Multi-objective optimization with weighted scoring of these metrics indicates that Ring trap geometry scores the highest total (80.2/100), followed by Linear Paul (72.0), Segmented (67.0) and Surface (55.1) configurations. ANOVA (one-way) statistically verified the presence of statistically significant inter-geometry differences in micro-motions amplitude (F = 18.43, p < .001) and cooling efficiency (F = 22.17, p < .001), with Tukey HSD post-hoc revealed that the most significant difference was between the Ring and the Surface. The Pearson correlation analysis shows that RF drive voltage and micromotion amplitude (r = -0.94 to -0.99) and cooling efficiency and micromotion (r = -1.00) have near-perfect inverse correlations, which prove that the quality of confinement is regulated by electrode geometry, RF amplitude, and spacing. These results give quantitative design principles of next-generation ion traps in quantum computing and atomic clock. Keywords: ion trap, micromotion minimization, RF/DC electrode optimization, laser cooling, Mathieu stability, secular frequency, quantum computing

Annual Methodological Archive Research Review•Jan 30, 2026•0 citations

Method for determination of technical noise contributions to ion motional heating

Jonathon A. Sedlacek, Jules Stuart, William Loh +4 more

Microfabricated Paul ion traps show tremendous promise for large-scale quantum information processing. However, motional heating of ions can have a detrimental effect on the fidelity of quantum logic operations in miniaturized, scalable designs. In many experiments, motional heating rates are ascribed solely to anomalous heating, which arises at least in part from electric field noise from the trap surface not attributable to any known mechanisms. Contributions to measured ion heating rates due to technical voltage noise present on the static (DC) and radio frequency (RF) electrodes—i.e., due to voltage sources, amplifiers, and other electrical components—can often be overlooked. We present a reliable method for determining the extent to which motional heating is dominated by technical voltage noise on the DC or RF electrodes. Also, we demonstrate that stray DC electric fields can shift the ion position such that technical noise on the RF electrode can significantly contribute to the motional heating rate. After minimizing the pseudopotential gradient experienced by the ion induced by stray DC electric fields, the motional heating due to RF technical noise can be significantly reduced.

arXiv:1805.09491•Journal of Applied Physics•Dec 6, 2018•18 citations

Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm

Moji Ghadimi, Alexander Zappacosta, Jordan Scarabel +3 more

Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text]Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text]S[Formula: see text]P[Formula: see text] Doppler cooling transition at 369.5 nm.

arXiv:2102.05830•Scientific Reports•Apr 29, 2022•2 citations

Precise micromotion compensation of a tilted ion chain

Craig Hogle, Ashlyn D. Burch, Jonathan David Sterk +9 more

Excess micromotion can be a substantial source of errors in trapped-ion based quantum processors and clocks due to the sensitivity of the internal states of the ion to external fields and motion. This problem can be fixed by compensating background electric fields in order to position ions at the RF node and minimize their driven micromotion. Here we describe techniques for compensating ion chains in scalable surface ion traps. These traps are capable of cancelling stray electric fields with fine spatial resolution in order to compensate multiple closely spaced ions due to their large number of relatively small control electrodes. We demonstrate a technique that compensates an ion chain to better than 5 V/m and within 0.1 degrees of chain rotation.

Frontiers in Quantum Science and Technology•Apr 22, 2024•3 citations
Minimizing the micromotion of a trapped ion in a linear Paul trap is of great importance in maintaining long coherence time as well as implementing quantum logic gates with high fidelity, which is crucial for large-scale quantum computation with trapped ions. Here, by applying the RF (radio frequency)-photon correlation technique, we demonstrate that a machine learning method based on artificial neural networks can quickly search for optimal voltage settings of the electrodes to minimize the trapped ion's micromotion. This machine learning assisted RF-photon correlation technique can be straightforwardly applied to more complicated surface ion traps with many electrodes, where the manual minimization of the excess micromotion generated by stray electric fields would become extremely challenging for the larger number of electrodes with various voltage settings. Instead, the presented machine learning assisted method provides an effective and automatic way to address this need.
For experiments with ions confined in a Paul trap, minimization of micromotion is often essential. In order to diagnose and compensate micromotion we have implemented a method that allows for finding the position of the radio-frequency (rf) null reliably and efficiently, in principle, without any variation of direct current (dc) voltages. We apply a trap modulation technique and focus-scanning imaging to extract three-dimensional ion positions for various rf drive powers and analyze the power dependence of the equilibrium position of the trapped ion. In contrast to commonly used methods, the search algorithm directly makes use of a physical effect as opposed to efficient numerical minimization in a high-dimensional parameter space. Using this method we achieve a compensation of the residual electric field that causes excess micromotion in the radial plane of a linear Paul trap down to $0.09\phantom{\rule{4pt}{0ex}}\text{V}\phantom{\rule{0.16em}{0ex}}{\text{m}}^{\ensuremath{-}1}$. Additionally, the precise position determination of a single harmonically trapped ion employed here can also be utilized for the detection of small forces. This is demonstrated by determining light pressure forces with a precision of 135 yN. As the method is based on imaging only, it can be applied to several ions simultaneously and is independent of laser direction and thus well suited to be used with, for example, surface-electrode traps.
Precise control of charged particles in radio-frequency (Paul) traps requires minimising excess micromotion induced by stray electric fields. We present a method to detect and compensate such fields through amplitude modulation of the radio-frequency trapping field. Modulation at frequencies close to the motional modes of the trapped particle excites coherent motion whose amplitude linearly depends on the stray field. In trapped-ion experiments, this motion can be detected by recording the arrival times of photons scattered during laser cooling. Only a single laser beam is required to resolve fields in multiple directions. In a demonstration using a 88Sr+^{88}\mathrm{Sr}^{+}88Sr+ ion in a surface electrode trap, we achieve a sensitivity of 0.1 V m−1 / Hz0.1\, \mathrm{V}\, \mathrm{m}^{-1}\, /\, \sqrt{\mathrm{Hz}}0.1Vm−1/Hz​ and a minimal uncertainty of 0.015 V m−10.015\, \mathrm{V}\, \mathrm{m}^{-1}0.015Vm−1.
Ion traps are a foundational technology in quantum information processing, precision spectroscopy and foundational physics tests. At the heart of their performance is the inhibition of micromotion, an oscillation of ions through imperfect trapping potentials, that restricts the laser cooling efficiency, coherence times, and the accuracy of measurements. The paper provides a systematic computational analysis of four different electrode geometries (Linear Paul, Segmented, Surface and Ring) in 24 different RF / DC configurations (C01-C24) and 13 performance parameters that comprise trap depth (0.62 1.48 eV), radial secular frequency (1,100 1 Multi-objective optimization with weighted scoring of these metrics indicates that Ring trap geometry scores the highest total (80.2/100), followed by Linear Paul (72.0), Segmented (67.0) and Surface (55.1) configurations. ANOVA (one-way) statistically verified the presence of statistically significant inter-geometry differences in micro-motions amplitude (F = 18.43, p < .001) and cooling efficiency (F = 22.17, p < .001), with Tukey HSD post-hoc revealed that the most significant difference was between the Ring and the Surface. The Pearson correlation analysis shows that RF drive voltage and micromotion amplitude (r = -0.94 to -0.99) and cooling efficiency and micromotion (r = -1.00) have near-perfect inverse correlations, which prove that the quality of confinement is regulated by electrode geometry, RF amplitude, and spacing. These results give quantitative design principles of next-generation ion traps in quantum computing and atomic clock. Keywords: ion trap, micromotion minimization, RF/DC electrode optimization, laser cooling, Mathieu stability, secular frequency, quantum computing
Microfabricated Paul ion traps show tremendous promise for large-scale quantum information processing. However, motional heating of ions can have a detrimental effect on the fidelity of quantum logic operations in miniaturized, scalable designs. In many experiments, motional heating rates are ascribed solely to anomalous heating, which arises at least in part from electric field noise from the trap surface not attributable to any known mechanisms. Contributions to measured ion heating rates due to technical voltage noise present on the static (DC) and radio frequency (RF) electrodes—i.e., due to voltage sources, amplifiers, and other electrical components—can often be overlooked. We present a reliable method for determining the extent to which motional heating is dominated by technical voltage noise on the DC or RF electrodes. Also, we demonstrate that stray DC electric fields can shift the ion position such that technical noise on the RF electrode can significantly contribute to the motional heating rate. After minimizing the pseudopotential gradient experienced by the ion induced by stray DC electric fields, the motional heating due to RF technical noise can be significantly reduced.
Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text]Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text]S[Formula: see text]P[Formula: see text] Doppler cooling transition at 369.5 nm.
Excess micromotion can be a substantial source of errors in trapped-ion based quantum processors and clocks due to the sensitivity of the internal states of the ion to external fields and motion. This problem can be fixed by compensating background electric fields in order to position ions at the RF node and minimize their driven micromotion. Here we describe techniques for compensating ion chains in scalable surface ion traps. These traps are capable of cancelling stray electric fields with fine spatial resolution in order to compensate multiple closely spaced ions due to their large number of relatively small control electrodes. We demonstrate a technique that compensates an ion chain to better than 5 V/m and within 0.1 degrees of chain rotation.