54 research outputs found
Identification and Mitigation of Conducting Package Losses for Quantum Superconducting Devices
Low-loss superconducting microwave devices are required for quantum
computation. Here, we present a series of measurements and simulations showing
that conducting losses in the packaging of our superconducting resonator
devices affect the maximum achievable internal quality factors (Qi) for a
series of thin-film Al quarter-wave resonators with fundamental resonant
frequencies varying between 4.9 and 5.8 GHz. By utilizing resonators with
different widths and gaps, we sampled different electromagnetic energy volumes
for the resonators affecting Qi. When the backside of the sapphire substrate of
the resonator device is adhered to a Cu package with a conducting silver glue,
a monotonic decrease in the maximum achievable Qi is found as the
electromagnetic sampling volume is increased. This is a result of induced
currents in large surface resistance regions and dissipation underneath the
substrate. By placing a hole underneath the substrate and using superconducting
material for the package, we decrease the ohmic losses and increase the maximum
Qi for the larger size resonators
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
Multi-task learning (MTL) is a common paradigm that seeks to improve the
generalization performance of task learning by training related tasks
simultaneously. However, it is still a challenging problem to search the
flexible and accurate architecture that can be shared among multiple tasks. In
this paper, we propose a novel deep learning model called Task Adaptive
Activation Network (TAAN) that can automatically learn the optimal network
architecture for MTL. The main principle of TAAN is to derive flexible
activation functions for different tasks from the data with other parameters of
the network fully shared. We further propose two functional regularization
methods that improve the MTL performance of TAAN. The improved performance of
both TAAN and the regularization methods is demonstrated by comprehensive
experiments.Comment: To appear in AAAI-202
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