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

Improving Ion Trap Modeling.

Jay Wesley Van Der Wall

Published Dec 1, 2022• 0 citations
Publisher
DOI
10.2172/2006245

Related papers

Ion Traps

Hugh A. Klein

Methods of experimental physics•Jan 1, 1995•2 citations

Improved description of trapped ions as a modular electromechanical system

Noah Van Horne, Manas Mukherjee

Trapped ions are among the leading candidates for quantum computing technologies. Interfacing ion qubits in separate traps and interfacing ion qubits with superconducting qubits are two of the many challenges to scale up quantum computers. One approach to overcome both problems is to use a conducting wire to mediate the Coulomb interaction between ions in different traps or between ions and superconducting qubits. To this end, a trapped charged particle inducing charge on a conductor has long been modeled as a system of equivalent lumped element electronic components. Our careful consideration reveals two assumptions in the derivation of this model, which are generally unjustified. We explain how these assumptions undermine the model’s validity, and then introduce an improved way to use linear relationships to describe the interaction of trapped ions with nearby conductors. The new method reproduces results equivalent to those of other works that are not based on the circuit element model. This method is targeted for trouble-shooting experimental designs and allows experiments to test and compare the accuracy of different theoretical models.

Journal of Applied Physics•Apr 18, 2024•1 citation

Using machine learning to improve multi-qubit state discrimination of trapped ions from uncertain EMCCD measurements

Junho Jeong, Changhyun Jung, Taehyun Kim +1 more

This paper proposes a residual network (ResNet)-based convolutional neural network (CNN) model to improve multi-qubit state measurements using an electron-multiplying charge-coupled device (EMCCD). The CNN model is developed to simultaneously use the intensity of pixel values and the shape of ion images in determining the quantum states of ions. In contrast, conventional methods use only the intensity values. In our experiments, the proposed model achieved a 99.53±0.14% mean individual measurement fidelity (MIMF) of 4 trapped ions, reducing the error by 46% when compared to the MIMF of maximum likelihood estimation method of 99.13±0.08%. In addition, it is experimentally shown that the model is also robust against the ion image drift, which was tested by intentionally shifting the ion images.

Optics Express•Aug 24, 2023•3 citations

Ion Traps

Daniel F. Walls, Gerard J Milburn

Springer eBooks•Dec 6, 2007•0 citations

Improved Linear-Ion-Trap Frequency Standard

John D. Prestage

Improved design concept for linear-ion-trap (LIT) frequency-standard apparatus proposed. Apparatus contains lengthened linear ion trap, and ions processed alternately in two regions: ions prepared in upper region of trap, then transported to lower region for exposure to microwave radiation, then returned to upper region for optical interrogation. Improved design intended to increase long-term frequency stability of apparatus while reducing size, mass, and cost.

NASA Tech Briefs•Mar 1, 1995•0 citations

Ion Traps

Daniel F. Walls, Gerard J Milburn

Graduate texts in physics•Jan 1, 2025•0 citations
Trapped ions are among the leading candidates for quantum computing technologies. Interfacing ion qubits in separate traps and interfacing ion qubits with superconducting qubits are two of the many challenges to scale up quantum computers. One approach to overcome both problems is to use a conducting wire to mediate the Coulomb interaction between ions in different traps or between ions and superconducting qubits. To this end, a trapped charged particle inducing charge on a conductor has long been modeled as a system of equivalent lumped element electronic components. Our careful consideration reveals two assumptions in the derivation of this model, which are generally unjustified. We explain how these assumptions undermine the model’s validity, and then introduce an improved way to use linear relationships to describe the interaction of trapped ions with nearby conductors. The new method reproduces results equivalent to those of other works that are not based on the circuit element model. This method is targeted for trouble-shooting experimental designs and allows experiments to test and compare the accuracy of different theoretical models.
This paper proposes a residual network (ResNet)-based convolutional neural network (CNN) model to improve multi-qubit state measurements using an electron-multiplying charge-coupled device (EMCCD). The CNN model is developed to simultaneously use the intensity of pixel values and the shape of ion images in determining the quantum states of ions. In contrast, conventional methods use only the intensity values. In our experiments, the proposed model achieved a 99.53±0.14% mean individual measurement fidelity (MIMF) of 4 trapped ions, reducing the error by 46% when compared to the MIMF of maximum likelihood estimation method of 99.13±0.08%. In addition, it is experimentally shown that the model is also robust against the ion image drift, which was tested by intentionally shifting the ion images.