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Found 15 papers, 4 papers with code
Explainable Classification Techniques for Quantum Dot Device Measurements
In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information science, which we consider here.
Data Needs and Challenges of Quantum Dot Devices Automation: Workshop Report
Gate-defined quantum dots are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers.
QDA$^2$: A principled approach to automatically annotating charge stability diagrams
Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing.
Extending Explainable Boosting Machines to Scientific Image Data
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern.
Automated extraction of capacitive coupling for quantum dot systems
Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform.
Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
The success rate for the action-based tuning consistently surpasses 95 % on both simulated and experimental data suitable for off-line testing.
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
We establish a dataset of over $1. 6\times10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research.
Colloquium: Advances in automation of quantum dot devices control
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers.
Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
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.
Toward Robust Autotuning of Noisy Quantum Dot Devices
In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module.
Theoretical bounds on data requirements for the ray-based classification
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases.
Ray-based framework for state identification in quantum dot devices
Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates.
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data.
Ray-based classification framework for high-dimensional data
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice.
Machine Learning techniques for state recognition and auto-tuning in quantum dots
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i. e. tuning up devices.
Quantum Physics
