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Found 10 papers, 3 papers with code
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.
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.
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.
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
Exponential improvements for quantum-accessible reinforcement learning
Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification.
Quantum-enhanced machine learning
Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements.
Framework for learning agents in quantum environments
In this paper we provide a broad framework for describing learning agents in general quantum environments.
