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

Nature

An atom-sorting machine

Y. Miroshnychenko, Wolfgang Alt, I. Dotsenko, Leonid Förster, Mkrtych Khudaverdyan, Dieter Meschede, D. Schrader, Arno Rauschenbeutel

Published Jul 12, 2006• 136 citations
Publisher
DOI
10.1038/442151a

Related papers

Single beam atom sorting machine

M. McGovern, Tzahi Grünzweig, Andrew J. Hilliard +1 more

We create two overlapping one-dimensional optical lattices using a single laser beam, a spatial light modulator and a high numerical aperture lens. These lattices have the potential to trap single atoms, and using the dynamic capabilities of the spatial light modulator may shift and sort atoms to a minimum atom-atom separation of 1.52 μm. We show how a simple feedback circuit can compensate for the spatial light modulator's intensity modulation.

arXiv:1108.2123•Laser Physics Letters•Nov 2, 2011•7 citations

Portable atom sorter

Journal of the Franklin Institute•Oct 1, 1949•0 citations

Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons

Shangjie Guo, Sophia M. Koh, A. R. Fritsch +2 more

In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, soldet, is publicly available as an open-source python package. soldet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.

arXiv:2111.04881•Physical Review Research•May 31, 2022•5 citations

Juggling Atoms

Anonymous

Focus•Dec 1, 1998•0 citations

The ultimate in atom manipulation

John Maddox

Nature•Jun 29, 1995•0 citations

Enhanced QSVM with elitist non‐dominated sorting genetic optimisation algorithm for breast cancer diagnosis

P Jose, Shanmugasundaram Hariharan, M. Vimaladevi +3 more

Abstract Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.

IET Quantum Communication•Oct 23, 2024•22 citations
In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, soldet, is publicly available as an open-source python package. soldet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.
Abstract Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.