Spin echo can be used to refocus random dynamical phases caused by inhomogeneities in control fields and thereby retain the purity of a spatial distribution of quantum spins. This technique for accurate spin control is an essential ingredient in many applications, such as nuclear magnetic resonance, magnetic resonance imaging, and quantum information processing. Here, we show how all the elements of a spin echo sequence can be performed at high speed by means of transitionless quantum driving. This technique promises accurate control of rapid quantum spin evolution. We apply the scheme to universal nonadiabatic geometric single- and two-qubit gates in a nuclear magnetic resonance setting.

We reveal that a synthetic photonic lattice based on coupled optical loops can be utilized as a feed-forward neural network for processing of optical pulse sequences in time domain. As a proof of concept, we train the optical system to restore an initial shape of the pulse train from the signal distorted due to linear dispersion in a fiber-optic link. We also show efficient training of the optical network with an intrinsic Kerr-type nonlinearity for the realization of target nonlinear transmission functions and inference functionality for the discrimination of different pulse sequences. The theoretical modeling is performed under practical conditions and can guide future experimental realizations.

Droplet evaporation is a versatile process to deposit solute materials on a substrate unless it induces solute accumulation at droplet perimeter because of outward capillary flows. Despite many efforts to generate inward Marangoni flows to suppress outward capillary flows, it is challenging to control Marangoni flows in colloidal droplets that include binary solvents. To understand and manipulate Marangoni flows in colloidal binary droplets, we investigate droplet evaporation and deposition dynamics by changing the mass ratio of binary solvents. We show that island deposits emerge when Marangoni flows overwhelm capillary flows during most evaporation. The formation of island deposits in colloidal binary droplets would be a helpful route to achieve uniform deposits of colloidal particles or nanoparticles.

We propose how to achieve strong phonon blockade (PB) in a hybrid spin-mechanical system in the weak-coupling regime. We demonstrate the implementation of magnetically induced two-phonon interactions between a mechanical cantilever resonator and an embedded nitrogen-vacancy center, which, when combined with parametric amplification of the mechanical motion, produces significantly enlarged anharmonicity in the eigenenergy spectrum. In the weak-driving regime, we show that strong PB appears in the hybrid system along with a large mean phonon number, even in the presence of strong mechanical dissipation. We also show flexible tunability of phonon statistics by controlling the strength of mechanical parametric amplification. Our work opens up prospects for the implementation of an efficient single-phonon source, with potential applications in quantum phononics and phononic quantum networks.

Few-layer metasurfaces, which are planar artificial arrays composed of more than one functional layer, have been showing unprecedented capabilities for the implementation of integrated and miniaturized optical devices with high efficiency and broad working bandwidth. However, the rich design freedoms of few-layer metasurfaces severely challenge their design and optimization. A universal strategy for the design of few-layer metasurfaces with different desired optical functionalities and an arbitrary number of layers, which can lower the design complexity and the time cost for structural optimization, is still eagerly anticipated by the scientific community. Here, we demonstrate an inverse design strategy based on deep-learning technology for the design of few-layer metasurfaces. By combining the matrix theory of multilayer optics, the proposed algorithm can predict the entire scattering matrix of a few-layer metasurface in tens of seconds with an acceptable accuracy and realize the inverse design of few-layer metasurfaces with different desired functionalities. Thus, the proposed inverse design strategy provides an efficient solution for the reduction of the design complexity of few-layer metasurfaces and significantly lowers the time cost for the structural optimization when compared with the numerical simulation methods based on an iterative process of trial and error, which will be of benefit to and expand the related research.

We investigate the electrical transport in amorphous oxide semiconductors by applying the percolation-based random band-edge model proposed by Nenashev et al. [Phys. Rev. B 100, 125202 (2019)] to multicationic, multianionic, and multinary materials. We modify the model by introducing the Hall-scattering factor. This allows us to evaluate, besides the conductivity, the temperature-dependent free charge-carrier concentration and Hall mobility, additionally enabling the inclusion of localized tail states in the model. The extended model allows analysis of pulsed-laser deposited amorphous zinc tin oxide, magnetron-sputtered amorphous zinc oxynitride, and zinc magnesium oxynitride thin films with carrier concentrations ranging from ${10}^{16}$ to ${10}^{19}{\mathrm{cm}}^{-3}$ at room temperature. Excellent agreement of the extended model data with the measured electrical properties is found for the investigated temperature regime of $50$ to $300\mathrm{K}$. In addition, we extract critical parameters of the random band-edge model and discuss them with regard to varying deposition parameters and different anion and cation concentrations. In particular, we find the standard deviation of the mobility edge distribution to be ranging between $20$ and $60\mathrm{meV}$ for the investigated materials.

Autocorrelated noise appears in many solid-state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of autocorrelated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly 2 orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.

Transition-metal dichalcogenides (TMDs) are promising candidates for a wide variety of ultrascaled electronic, optoelectronic, and quantum-computing applications. The electronic density of states exponentially decaying into the band gap (also known as the band tail) has a strong impact on the performance of TMD applications. In this work, the band tails of various TMD monolayer and multilayer systems is predicted with density-functional-theory-based nonequilibrium Green’s functions when placed on several dielectric substrates such as ${\mathrm{Hf}\mathrm{O}}_2$, ${\mathrm{Al}}_2{\mathrm{O}}_3$, ${\mathrm{Si}\mathrm{O}}_2$, and $h$-BN. Nonlocal scattering of electrons on polar optical phonons, charged impurities, and remote scattering on phonons in the dielectric materials is included in the self-consistent Born approximation. The band tails are found to critically depend on the layer thickness, temperature, doping concentration, and particularly on the chosen dielectric substrate. The underlying physical mechanisms are studied in high detail and an analytical interpolation formula is given to provide a quick reference for Urbach parameters and to guide the design work in ${\mathrm{Mo}\mathrm{S}}_2$, ${\mathrm{WS}}_2$, and ${\mathrm{WSe}}_2$.

We propose a practical biaxial signal decoupling method for the longitudinal magnetic-field-modulated spin-exchange-relaxation-free comagnetometer used in the inertial rotation measurement. The method is implemented based on a model, which had been verified by a rotation response experiment. According to our model, by adjusting the magnetic field in the pumping direction to the decoupling point, the modulated comagnetometer can independently output the rotation information of two orthogonal directions. The rotational scale factor also reaches the maximum value, and the rotation sensitivity and long-term stability can be further improved. Our method not only retains the advantages of the modulated comagnetometer that it can output the dual-axis signal simultaneously with only one probe beam, but also realizes biaxial signal decoupling, and achieves better practical performance.

We show using three-dimensional kinetic Monte Carlo simulations that the injection of charge carriers from a metallic electrode into a disordered organic semiconductor is under nominally Ohmic injection conditions strongly impeded by the short-range Coulomb interactions between the charge carriers in the image-force-stabilized interfacial dipole layer. In contrast, master equation and conventional one-dimensional drift-diffusion simulations underestimate these Coulomb interactions due to their mean-field approximation, and are found not to reveal the effect. The simulations predict a reduction of the current density in organic semiconductor devices when the nominal injection barrier is taken very small or even negative, consistent with recent experimental results [Kotadiya et al., Nat. Mater. 17, 329 (2018)]. However, whereas in that work a modification of the energetic disorder near the interface is assumed, we find that the effect is already obtained after including charge-charge interactions beyond a one-dimensional and mean-field approximation.

Noisy intermediate-scale quantum (NISQ) devices are nowadays starting to become available to the final user, hence potentially allowing the quantum speedups predicted by quantum-information theory to be shown. However, before implementing any quantum algorithm, it is crucial to have at least a partial or possibly full knowledge on the type and amount of noise affecting the quantum machine. Here, by generalizing quantum generative adversarial learning from quantum states (QGANs) to quantum operations, superoperators, and channels (here named super QGANs), we propose a very promising framework to characterize noise in a realistic quantum device, even in the case of spatially and temporally correlated noise (memory channels) affecting quantum circuits. The key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one. We find that, when applied to the benchmarking case of Pauli channels, the super-QGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise. Moreover, we also show how to employ it for quantum metrology applications. We believe our super QGANs pave the way for the development of hybrid quantum-classical machine-learning protocols for a better characterization and control of the current and future unavoidably noisy quantum devices.

In the fields of geology, materials and biomedical engineering, the noninvasive and accurate estimation of the dynamic mechanical properties of the soft matter enclosed in a rigid shell induced by a low-frequency vibration is worthy of exploration. The addition of fluidity on the basis of viscoelasticity to describe the properties of the soft matter, especially when containing a large amount of water, may be of great value in the detection of crustal movement, material structure, and early cerebral diseases. In the biomedical field, since the influence of the skull on ultrasound is unknown for viewing the propagation of the transcranial shear wave, it is challenging to obtain the mechanical properties of the brain tissue with the skull using the transcranial ultrasound. In this study, the propagations of the transcranial shear wave within the brain tissue induced by an external low-frequency vibration are presented by finite-element-method simulation. This experiment achieved a transcranial ultrasound estimation and differentiation of viscoelasticity and fluidity of brain phantoms enclosed in the skull using the low-frequency vibration and Kelvin-Voigt fractional derivative modeling, and the estimation results are consistent with the nontranscranial ones. These results represent a great potential in the estimation of viscoelasticity and fluidity of the soft matter under different boundary constraints in various areas.

The dipole response of helium exposed to attosecond extreme ultraviolet (EUV) pulses and a femtosecond near-infrared (NIR) laser field is investigated. The optical interference between the EUV pulse and nonlinear wave-mixing signals leads to fringes in the transmitted spectrum above the single ionization threshold. Perturbation theory suggests that such fringes record the self-convolution of complex-valued NIR pulse in the frequency domain, allowing direct characterization of a broadband optical pulse without the need of reconstruction algorithms. We apply this in situ interferometric scheme to diagnose a few-cycle laser pulse.

Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors. The key advantages of such devices are their high sensitivity, capability to work at low input power, and compactness. In this work, we show a possible use of STDs for neuromorphic computing to expand the realm of their functionalities for the implementation of analog multiplication, which is a key operation in convolutional neural networks (CNNs). In particular, we introduce the concept of degree of rectification (DOR) in injection-locked STDs. Micromagnetic simulations are used to design and identify the working range of the STDs for the implementation of the DOR. Previous experimental data confirm the applicability of the proposed solution, which is tested in image processing and in a CNN that recognizes handwritten digits.

In contrast to artificial metamaterials, natural two-dimensional (2D) hyperbolic materials are able to support higher electromagnetic confinement and stronger photonic density of states. This means natural 2D hyperbolic materials have fascinating potential for applications in thermal photonics. In this work, we investigate mechanisms of near-field thermal radiation (NFTR) in a ${\mathrm{T}}_d$–${\mathrm{WTe}}_2$ single layer at the nanoscale using the fluctuation-dissipation theorem and density-functional theory. The results show that NFTR of ${\mathrm{T}}_d$–${\mathrm{WTe}}_2$ can be 3 orders of magnitude larger than the blackbody limit. By using the photonic tunneling coefficient and the plasmon dispersion, we present a comprehensive study of plasmonic properties of ${\mathrm{T}}_d$–${\mathrm{WTe}}_2$. Moreover, our first-principles calculations predict that application of a certain amount of mechanical stress can trigger topological transition between the elliptic and the hyperbolic surface states by regulating the Fermi surface and interband excitation threshold. Lastly, we systematically exhibit the evolution of surface plasmon polaritons in ${\mathrm{T}}_d$–${\mathrm{WTe}}_2$ under mechanical stress and analyze the performance of using mechanical stress to modulate the corresponding NFTR. Our work explores the great potential of tunable radiative heat flux in ${\mathrm{T}}_d$–${\mathrm{WTe}}_2$, which is explained here through the peculiar nature of hyperbolic surface plasmons.

Two-dimensional (2D) metal-particle focusing is an essential task for various fabrication processes. While acoustofluidic devices can manipulate particles in two-dimensions, the production of these devices often demands a cleanroom environment. Therefore, acoustically excited glass capillaries present a cheap alternative to labor-intensive cleanroom production. Here, we present 2D metal microparticle focusing in a round glass capillary using bulk acoustic waves. Excitation of the piezoelectric transducer at specific frequencies leads to mode shapes in the round capillary, concentrating particles toward the capillary center. We experimentally investigate the particle line width for different particle materials and concentrations. We demonstrate the focus of copper particles approximately $1\mu \mathrm{m}$ in diameter down to a line of width $60.8\pm 7.0\mu \mathrm{m}$ and height $45.2\pm 9.3\mu \mathrm{m}$, corresponding to a local concentration of 4.5% v/v, which is $90$ times higher than the concentration of the initial solution. Further, we achieve the focusing of 1-$\mu \mathrm{m}$ polystyrene particles, which is usually prevented due to acoustic streaming. Through numerical analysis, we reveal the mechanism enabling the manipulation of particles in the low-micrometer range. Due to a transition of the acoustic streaming patterns from two dominant vortices in the lower half to two dominant vortices in the upper half, the streaming velocity exhibits a local minimum while the overall acoustic energy density stays at a sufficiently high level for particle focusing, leading to a lower critical particle radius than in conventional rectangular microchannels. Finally, we use our method to eject copper particles through a tapered round capillary with an opening of $25\mu \mathrm{m}$ in diameter, which would not be possible without particle focusing. Our setup can be utilized for various applications that otherwise might suffer from abrasion, clogging, and limited resolution.

Insulator-metal phase oscillations driven by direct voltages in strongly correlated materials, which can naturally emulate nonlinear neural behavior, have hitherto been realized largely as extrinsic charging and discharging cycles of capacitors with limited frequencies. Here, based on an experimentally validated physical phase-field description of the insulator-metal transition, we demonstrate an intrinsic noncapacitive insulator-metal phase oscillation in a prototypical strongly correlated material, ${\mathrm{VO}}_2$, near room temperature, which can be generic in Mott insulators. Such intrinsic phase oscillations exhibit frequencies $1$–$2$ orders of magnitude higher than the typical frequencies of the extrinsic capacitive phase oscillations. They manifest themselves as electronically driven automatic growth and shrinkage of conduction filaments in contrast to the usual suggestion of thermally driven growth. The discovery of intrinsic phase oscillations has important implications for exploring the intrinsic nonlinear electronic dynamics in strongly correlated materials and advances the realization of high-frequency Mott electronic oscillators for neuromorphic computing.

In the presence of Rashba-Dresselhaus coupling, strong spin-orbit interactions in liquid-crystal optical cavities result in a distinctive spin-split entangled dispersion. Spin coherence between such modes gives rise to an optically persistent spin helix. In this paper, we introduce optical gain in such a system, by dispersing a molecular dye in a liquid-crystal microcavity, and demonstrate an optically persistent spin-helix lasing in the Rashba-Dresselhaus regime.

Mott insulators are a class of strongly correlated materials with emergent properties important for modern electronics applications, such as artificial neural networks. Under an electric field, these compounds undergo a resistive switching that may be used to build up artificial neurons. However, the mechanism of this resistive switching is still under debate and may depend on the Mott material involved. Some works suggest an electronic avalanche phenomenon, while others propose an electrothermal scenario. As electric pulses produce both Joule heating and hot carriers, disentangling their respective roles requires the use of another external stimulus. Here, an ultrashort light pulse is used to tune the number of photogenerated carriers and the energy provided to the system. In these pump-pump-probe experiments, a crystal of the Mott insulator ${\mathrm{Ga}\mathrm{Ta}}_4{\mathrm{Se}}_8$ is simultaneously excited by electric and laser pulses while an electric probe monitors its conductivity. The study shows that the resistive switching is affected by the number of generated photocarriers rather than by the accumulation of energy deposited by the femtosecond laser. It supports therefore a mechanism driven by generation of hot carriers. Finally, our work opens the possibility to build up an artificial electro-optical “Mott” neuron tuned by a femtosecond laser pulse.

On-chip mode conversion is beneficial in photonic integrations and enables many functionalities for optical signal processing, however, it is still challenging to achieve a robust solution with broadband working wavelengths. Here, by exploring an interesting coupling effect between triplet and singlet waveguides, we propose and realize a robust mode conversion based on an undistributed breathing mode in the waveguide triplet supercell. It is well demonstrated with high efficiency and robustness in the silicon waveguide platform, which shows further extensions to higher-order mode conversions by connecting more elements together. Our approach makes good use of the undisturbed breathing modes in a three-level system for robust mode conversions, which is proved to be an effective means and promises further functional elements in photonic integrations.