Quantitative complementarity of wave-particle duality

To test the principle of complementarity and wave-particle duality quantitatively, we need a quantum composite system that can be controlled by experimental parameters. Here, we demonstrate that a double-path interferometer consisting of two parametric downconversion crystals seeded by coherent idler fields, where the generated coherent signal photons are used for quantum interference and the conjugate idler fields are used for which-path detectors with controllable fidelity, is useful for elucidating the quantitative complementarity. We show that the quanton source purity μs is tightly bounded by the entanglement E between the quantons and the remaining degrees of freedom by the relation μs=1−E2, which is experimentally confirmed. We further prove that the experimental scheme using two stimulated parametric downconversion processes is an ideal tool for investigating and understanding wave-particle duality and Bohr’s complementarity quantitatively.

A self-supervised Deep Learning approach for improving signal coherence in Distributed Acoustic Sensing

<p>Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis as well as in monitoring of urban and marine environments, including microseismicity detection, ambient noise tomography, traffic density monitoring, and maritime vessel tracking. A major advantage of DAS is its ability to turn fibre-optic cables into large and dense seismic arrays. As a cornerstone of seismic array analysis, beamforming relies on the relative arrival times of coherent signals along the optical fibre array to estimate the direction-of-arrival of the signals, and can hence be used to locate earthquakes as well as moving acoustic sources (e.g. maritime vessels). Naturally, this technique can only be applied to signals that are sufficiently coherent in space and time, and so beamforming benefits from signal processing methods that enhance the signal-to-noise ratio of the spatio-temporally coherent signal components. DAS measurements often suffer from waveform incoherence, and processing submarine DAS data is particularly challenging.</p><p>In this work, we adopt a self-supervised deep learning algorithm to extract locally-coherent signal components. Owing to the similarity of coherent signals along a DAS system, one can predict the coherent part of the signal at a given channel based on the signals recorded at other channels, referred to as "J-invariance". Following the recent approach proposed by Batson & Royer (2019), we leverage the J-invariant property of earthquake signals recorded by a submarine fibre-optic cable. A U-net auto-encoder is trained to reconstruct the earthquake waveforms recorded at one channel based on the waveforms recorded at neighbouring channels. Repeating this procedure for every measurement location along the cable yields a J-invariant reconstruction of the dataset that maximises the local coherence of the data. When we apply standard beamforming techniques to the output of the deep learning model, we indeed obtain higher-fidelity estimates of the direction-of-arrival of the seismic waves, and spurious solutions resulting from a lack of waveform coherence and local seismic scattering are suppressed.</p><p>While the present application focuses on earthquake signals, the deep learning method is completely general, self-supervised, and directly applicable to other DAS-recorded signals. This approach facilitates the analysis of signals with low signal-to-noise ratio that are spatio-temporally coherent, and can work in tandem with existing time-series analysis techniques.</p><p>References:<br>Batson J., Royer L. (2019), "Noise2Self: Blind Denoising by Self-Supervision", Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California</p>

Observation of quadrocopters by radar with long-term coherent signal accumulation

The article discusses the experimentally obtained characteristics of radar signals reflected from small-sized aerial targets such as a quadrocopter, with their long-term coherent accumulation. A brief description of the structural diagram of the experimental radar and its characteristics is given. The radar operates in the ten-centimeter wavelength range and emits a coherent-pulse signal. The duration of the emitted chirp pulse is 1 μs with a compression ratio of 15. Algorithms for primary processing of signals in a computer are given, including compression of chirp signals and spectral analysis of the received implementation, which is equivalent to its coherent accumulation. The parameters of the generated radar image are determined. The characteristics of the targets used - the small-sized quadcopters Mavic and Phoenix – are given. As a result of the experiments, it was shown that the tested small-sized air targets in the ten-centimeter wavelength range of the probing signal have their own coherence time sufficient for the coherent accumulation of the signal reflected from them for a time of at least 0.2 seconds. The Mavic does not produce reflections from its rotating rotors. The main rotor of the Phoenix quadcopter creates spectral components in the image, concentrated along the speed axis in the form of maxima symmetrically located relative to the central mark of the target. The presence of this feature of the signal allows you to identify the type of target, highlight the target against the background of birds, and detect a stationary, hovering target. It is shown that the features of signals reflected from the ground, with long-term coherent accumulation, allow providing the minimum speed of the detected target, measured in fractions of a meter per second.