MICROWAVE KINETIC INDUCTANCE DETECTOR SIGNAL DENOISING USING MACHINE LEARNING
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Date
2024
Authors
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Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Superconducting micro-resonators are crucial in creating superposition states in
quan tum computers and are gaining prominence as quantum technology
evolves. Beyond their quantum applications, these resonators are instrumental as
ultra-sensitive de tectors in astronomy, particularly Microwave Kinetic Inductance
Detectors (MKIDs), which are key for both scientific exploration and industrial
use. However, the is sue of electronic noise in these detectors remains a
significant hurdle. To address this, our approach integrates machine learning
strategies alongside conventional noise reduction methods to improve MKID
signal fidelity.
Our process involves gathering data and applying denoising models, with a
strong emphasis on machine learning techniques. Our findings highlight the
strengths of dif ferent denoising techniques, particularly deep learning
architectures like Long Short Term Memory (LSTM) networks and Autoencoders,
which demonstrate promising results in denoising MKID signals. These models
exhibit adaptability to diverse noise sources, proficiency in identifying complex
noise patterns, and continuous improve ment with additional data.
This research represents a significant advancement in the field and provides
es sential insights into improving MKID detectors, which in turn could lead to
ground breaking developments in various scientific areas.
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Keywords
Type of access: Restricted, Superconducting micro-resonators, Microwave Kinetic In ductance Detectors (MKIDs), denoising, electronic noise, signal reliability
Citation
Maksut, Zh. (2024). Microwave Kinetic Inductance Detector Signal Denoising Using Machine Learning. Nazarbayev University School of Engineering and Digital Sciences