Controlling the formation of bimetallic heterogeneous nanogaps structures have many applications in the plasmonics and catalysis fields. Here, a simple and systematic method is developed to fabricate tunable and stable Au–Ag nanowire‐based plasmonic metamaterials. The sub‐10 nm Au–Ag bimetallic heterogeneous nanogaps with desirable optical properties are fabricated by a simple, ultrarapid, and robust nanoskiving technique. Compared to the monometallic linear Ag–Ag and Au–Au nanogaps, the Au–Ag bimetallic heterogeneous nanogaps exhibit remarkable surface enhanced Raman spectroscopy (SERS) enhancement properties due to the nanogaps between the adjacent Au/Ag nanowires, and the Ag/Au bimetallic composite film. In addition, 3D bimetallic heterogeneous nanogaps are built and produce much stronger electric fields than those of the 1D linear nanogaps. The sub‐10 nm Au–Ag heterogeneous nanogaps are promising to be used in SERS substrate, plasmon devices, catalysis, and printed electronics.

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Quantum metrology is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use a circuit learning approach to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics.Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate a 1.69x SNR improvement over the classical limit on a 5-qubit IBM quantum computer. More notably, our algorithm overcomes the plateauing (or even decreasing) performance of existing entanglement-based protocols with increased system sizes.

A scalable fabrication route combining colloidal lithography and nanoskiving is reported for generating free‐standing asymmetric metal nanostructures of crescent‐shaped gold nanowires and rows of opposing crescents with and without nanogaps. Strong localized surface plasmon resonances and propagating surface plasmon polaritons are excited at the sharp tips of the crescent and in the sub‐10 nm nanogaps. High‐order resonance modes are excited due to the coupling between the resonances in the tips and gaps. The Raman signals are greatly enhanced due to the strong electric fields. In addition, the optical responses and electric field distributions can be controlled by the polarization of the incident light. The strong electric field enhancement coupled with facile, scalable fabrication make crescent‐shaped nanostructures promising in nonlinear optics, optical trapping, and surface‐enhanced spectroscopy.

This paper explores the application of a wavelet neural network (WNN), whose hidden layer is comprised of neurons with adjustable wavelets as activation functions, to stock prediction. We discuss some basic rationales behind technical analysis, and based on which, inputs of the prediction system are carefully selected. This system is tested on Istanbul Stock Exchange National 100 Index and compared with traditional neural networks. The results show that the WNN can achieve very good prediction accuracy.