Deep learning for deep waters: An expert-in-the-loop machine learning framework for marine sciences

Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences

Andreev conductance of a domain wall

At low temperatures, the transport through a superconductor-ferromagnet tunnel interface is due to tunneling of electrons in pairs. Exchange field of a monodomain ferromagnet aligns electron spins and suppresses the two electron tunneling. The presence of the domain walls at the SF interface strongly enhances the subgap current. The Andreev conductance is proven to be proportional to the total length of domain walls at the SF interface.Comment: 4 pages and 1 figur

Ferromagnetic Josephson Junctions for High Performance Computation

Josephson junctions drive the operation of superconducting qubits and they are the key for the coupling and the interfacing of superconducting qubit components with other quantum platforms. They are the only means to introduce non linearity in a superconducting circuit and offer direct solutions to tune the properties of a superconducting qubit, thus enlarging the possible qubit layouts. Junctions performances and tunability can take advantage of using a large variety of barriers and their special functionalities. We mention pertinent results on the advances in understanding the properties of ferromagnetic junctions, which make possible the use of these devices either as memory elements and as core circuit elements

Ferromagnetic Josephson switching device with high characteristic voltage

We develop a fast Magnetic Josephson Junction (MJJ) - a superconducting ferromagnetic device for a scalable high-density cryogenic memory compatible in speed and fabrication with energy-efficient Single Flux Quantum (SFQ) circuits. We present experimental results for Superconductor-Insulator-Ferromagnet-Superconductor (SIFS) MJJs with high characteristic voltage IcRn of >700 uV proving their applicability for superconducting circuits. By applying magnetic field pulses, the device can be switched between MJJ logic states. The MJJ IcRn product is only ~30% lower than that of conventional junction co-produced in the same process, allowing for integration of MJJ-based and SIS-based ultra-fast digital SFQ circuits operating at tens of gigahertz.Comment: 10 pages, 4 figure

Properties of ferromagnetic Josephson junctions for memory applications

In this work we give a characterization of the RF effect of memory switching on Nb-Al/AlOx-(Nb)-Pd$_{0.99}$Fe$_{0.01}$-Nb Josephson junctions as a function of magnetic field pulse amplitude and duration, alongside with an electrodynamical characterization of such junctions, in comparison with standard Nb-Al/AlOx-Nb tunnel junctions. The use of microwaves to tune the switching parameters of magnetic Josephson junctions is a step in the development of novel addressing schemes aimed at improving the performances of superconducting memories.Comment: IEEE Trans. Appl. Supercond. Special Issue ISEC201

Ultrastrong photon-to-magnon coupling in multilayered heterostructures involving superconducting coherence via ferromagnetic layers

The critical step for future quantum industry demands realization of efficient information exchange between different-platform hybrid systems that can harvest advantages of distinct platforms. The major restraining factor for the progress in certain hybrids is weak coupling strength between the elemental particles. In particular, this restriction impedes a promising field of hybrid magnonics. In this work, we propose an approach for realization of on-chip hybrid magnonic systems with unprecedentedly strong coupling parameters. The approach is based on multilayered microstructures containing superconducting, insulating, and ferromagnetic layers with modified photon phase velocities and magnon eigenfrequencies. The enhanced coupling strength is provided by the radically reduced photon mode volume. Study of the microscopic mechanism of the photon-to-magnon coupling evidences formation of the long-range superconducting coherence via thick strong ferromagnetic layers in superconductor/ferromagnet/superconductor trilayer in the presence of magnetization precession. This discovery offers new opportunities in microwave superconducting spintronics for quantum technologies