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Register a new userQudi: a modular python suite for experiment control and data processing
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Qudi is a general, modular, multi-operating system suite written in Python 3 for controlling laboratory experiments. It provides a structured environment by separating functionality into hardware abstraction, experiment logic and user interface layers. The core feature set comprises a graphical user interface, live data visualization, distributed execution over networks, rapid prototyping via Jupyter notebooks, configuration management, and data recording. Currently, the included modules are focused on confocal microscopy, quantum optics and quantum information experiments, but an expansion into other fields is possible and encouraged. Qudi is available from https://github.com/Ulm-IQO/qudi and is freely useable under the GNU General Public Licence.
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Since the 1990s, there has been a dramatic interest in quantum communication. Free-space quantum communication is being developed to ultra-long distance quantum experiment, which requires higher electronics performance, such as time measurement precision, data-transfer rate, and system integration density. As part of the ground station of quantum experiment satellite that will be launched in 2016, we specifically designed a compact PCI-based multi-channel electronics system with high time-resolution, high data-transfer-rate. The electronics performance of this system was tested. The time bin size is 23.9ps and the time precision root-mean-square (RMS) is less than 24ps for 16 channels. The dead time is 30ns. The data transfer rate to local computer is up to 35 MBps, and the count rate is up to 30M/s. The system has been proven to perform well and operate stably through a test of free space quantum key distribution (QKD) experiment.
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Electromagnetic (EM) sensing is a wide-spread contactless examination technique in science, engineering and military. However, conventional sensing systems are mostly lack of intelligence, which not only require expensive hardware and complicated computational algorithms, but also pose important challenges for advanced in-situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition, and integrating it into a data-driven learnable data processing pipeline. This strategy allows to learn an optimal sensing chain in systematic sense of variational autoencoder, i.e., to jointly learn an optimal measurement strategy along with matching data post processing schemes. A three-port deep artificial neural network (ANN) is designed to characterize the measurement process, such that an optimal measurement strategy is adaptive to the subject of interest by controlling the programmable metasurface for manipulating the EM illuminations. We design and fabricate a proof-of-principle sensing system in microwave, and demonstrate experimentally its significance on the high-quality imaging and high-accuracy object recognition from a remarkably reduced number of measurements. We faithfully expect that the presented methodology will provide us with a fundamentally new perspective on the design of intelligent sensing architectures at various frequencies, and beyond.
The optical fibre is an essential tool for our communication infrastructure since it is the main transmission channel for optical communications. The latest major advance in optical fibre technology is spatial division multiplexing (SDM), where new fibre designs and components establish multiple co-existing data channels based on light propagation over distinct transverse optical modes. Simultaneously, there have been many recent developments in the field of quantum information processing (QIP), with novel protocols and devices in areas such as computing, communication and metrology. Here, we review recent works implementing QIP protocols with SDM optical fibres, and discuss new possibilities for manipulating quantum systems based on this technology.
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