Pulsed
Large-scale qubit readout in quantum computing
systems requires highly sensitive amplification with minimal
power consumption to reduce thermal load and preserve qubit
integrity. We propose a pulse-operated cryogenic low-noise am-
plifier (LNA) scheme that minimizes the influence from the LNA
on qubit operation and reduces power consumption by duty
cycling. A modified commercially available cryogenic hybrid LNA
based on InP HEMTs has been characterized to demonstrate the
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Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Detection and classification of these interference signals allow better situational awareness and facilitate appropriate countermeasures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This dataset is the result of a proposal for a low-resource interference detection and classification approach that combines conventional statistical signal processing approaches with machine learning (ML).
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