Nanomagnetic logic, in which the outcome of a computation is embedded into the energy hierarchy of magnetostatically coupled nanomagnets, offers an attractive pathway to implement in-memory computation. This computational paradigm avoids energy costs associated with storing the outcome of a computational operation. Thermally driven nanomagnetic logic gates, which are driven solely by the ambient thermal energy, hold great promise for energy-efficient operation, but have the disadvantage of slow operating speeds due to the slowness and lack of spatial selectivity of currently employed global heating methods. As has been shown recently, this disadvantage can be removed by employing plasmon-assisted photoheating, where selective local heating is achieved by the polarization dependence of the optical absorption cross section of the nanomagnet. Here, we show by means of micromagnetic and finite-element simulations how such local heating can be exploited to design reconfigurable nanomagnetic Boolean logic gates. The reconfigurability of operation is achieved either by modifying the initializing field protocol or optically, by changing the order in which two orthogonally polarized laser pulses are applied. Our results thus demonstrate that nanomagnetic logic offers itself as a fast (up to gigahertz), energy-efficient and reconfigurable platform for in-memory computation that can be controlled via optical means.

Magnetic skyrmions are nanometric spin textures characterized by a quantized topological invariant in magnets and often emerge in a crystallized form called a skyrmion crystal in an external magnetic field. We propose that magnets hosting a skyrmion crystal possess high potential for application to reservoir computing, which is one of the most successful information processing techniques inspired by functions of the human brain. Our skyrmion-based reservoir exploits precession dynamics of magnetizations, i.e., spin waves, propagating in the skyrmion crystal. Because of complex interferences and slow relaxations of the spin-wave dynamics, the skyrmion spin-wave reservoir attains several important characteristics required for reservoir computing, e.g., the generalization ability, nonlinearity, and short-term memory. We investigate these properties by imposing three standard tasks to test the performance of the reservoir, i.e., the duration-estimation task, short-term memory task, and parity-check task. Through these investigations, we demonstrate that magnetic skyrmion crystals are promising materials for spintronics reservoir devices. Because magnetic skyrmions emerge spontaneously in magnets via a self-organization process under application of a static magnetic field, the proposed skyrmion reservoir requires neither advanced nanofabrication nor complicated manufacturing for production, in contrast to other previously proposed magnetic reservoirs constructed with fabricated spintronics devices. Our proposal is expected to realize a breakthrough in the research of spintronics reservoirs of high performance.

The interfacing of a superconducting microwave resonator with rare-earth-doped crystals presents a promising hybrid quantum system for applications including spin-assisted transducers and memories. The coupling strength between the spins of the rare-earth ions and the microwave photons is characterized by the cooperativity. Here, we report an ultrahigh cooperativity of $C\sim 650$ between rare-earth spins and a planar superconducting microwave resonator that features a highly uniform magnetic field for harnessing the strong anisotropic coupling strength of erbium-doped yttrium orthosilicate. This cooperativity rivals that from a bulk dielectric resonator and may pave a path for efficient coupling of spins with microwave photons on an integrated platform.

In this paper we present an analytical model for large displacements of electrostatically actuated elastic microbeams, including all their stable and unstable states. Such microbeams are the core element of microelectromechanical system-based resonators, transducers, and actuators. Their accurate modeling is essential to their development. Especially in the realm of system design, there is a need for lumped-parameter models with a single degree of freedom, where a large number of such subsystems can interact with each other. The state of the art suggests using zero-mode approximations far from the pull-in limit or using multi-degree-of-freedom Galerkin projection to get the results close to the pull-in limit. The challenges of finding a closed-form solution lie in the complicated structure of the Coulomb integral. Our approach to evaluating this integral is based on a Chebyshev-Edgeworth-type method common in analytic probability theory and allows the states to be accurately modeled where the kernel is close to the singularity. Although this approach to nonlinear dynamical systems is used here for a very specific purpose, where we report a user-friendly closed solution of a clamped-clamped microbeam, it has a much broader scope. It is apt to analyze different boundary conditions, electrostatic fringe field corrections, and squeeze film damping, to name a few applications. Moreover, the method presented here can generally be applied to the evaluation of singular integral kernels.

We successfully use two-dimensional hexagonal boron nitride (h-$\mathrm{BN}$) as a continuous and ultrathin insulator to facilitate the conversion of excitons into free carriers at organic donor-acceptor (D-A) interfaces. Monolayer- (0.33 nm) thick h-$\mathrm{BN}$ with a lateral size on the order of 1 ${\mathrm{cm}}^2$ is inserted between zinc phthalocyanine ($\mathrm{Zn}$Pc) and perylenetetracarboxylic diimide (PTCDI) organic films that form a generic D-A interface. We find that h-$\mathrm{BN}$ increases the photon-to-free-carrier conversion yield of the D-A heterostructure by 130% compared with identical samples without a h-$\mathrm{BN}$ layer, even though h-$\mathrm{BN}$ lowers the initial electron-transfer rate from $\mathrm{Zn}$Pc to PTCDI. The enhanced photon-to-free-carrier conversion yield is attributed to a larger charge-separation yield of charge-transfer (CT) excitons at the D-A interface. h-$\mathrm{BN}$ can prohibit the formation of tightly bound CT excitons and impede electron-hole recombination, which can improve the charge-separation yield.

Magnetic imaging with nitrogen-vacancy centers in diamond, also known as quantum diamond microscopy, has emerged as a useful technique for the spatial mapping of charge currents in solid-state devices. In this work, we investigate an application to photovoltaic (PV) devices, where the currents are induced by light. We develop a widefield nitrogen-vacancy microscope that allows independent stimulus and measurement of the PV device, and test our system on a range of prototype crystalline silicon PV devices. We first demonstrate micrometer-scale vector magnetic field imaging of custom PV devices illuminated by a focused laser spot, revealing the internal current paths in both short-circuit and open-circuit conditions. We then demonstrate time-resolved imaging of photocurrents in an interdigitated back-contact solar cell, detecting current buildup and subsequent decay near the illumination point with microsecond resolution. This work presents a versatile and accessible analysis platform that may find distinct application in research on emerging PV technologies.

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. We demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. Here we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 × 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

To conduct spin-sensitive transport measurements, a nonlocal device geometry is often used to avoid spurious voltages that are caused by the flow of charges. However, in the vast majority of reported nonlocal spin-valve, Hanle spin precession or spin Hall measurements, background signals have been observed that are not related to spins. We discuss seven different types of these charge-induced signals and explain how these artifacts can result in erroneous or misleading conclusions when falsely attributed to spin transport. The charge-driven signals can be divided into two groups: signals that are inherent to the device structure and/or the measurement setup and signals that depend on a common-mode voltage. We designed and built a voltage-controlled current source that significantly diminishes all spurious voltage signals of the latter group in both dc and ac measurements by creating a virtual ground within the nonlocal detection circuit. This is especially important for lock-in-based measurement techniques, where a common-mode voltage can create a phase-shifted, frequency-dependent signal with an amplitude several orders of magnitude larger than the actual spin signal. Measurements performed on graphene-based nonlocal spin-valve devices demonstrate how all spurious voltage signals that are caused by a common-mode voltage can be completely suppressed by such a current source.

We propose a protocol for the deterministic generation of entanglement between two ensembles of nuclear spins surrounding two distant quantum dots. The protocol relies on the injection of electrons with definite polarization, their sequential interaction with the nuclear ensembles of each quantum dot for a short time, and the coherent transfer of each electron from one quantum dot to the other. Computing the exact dynamics for small systems, and using an effective master equation and approximate nonlinear equations of motion for larger systems, we are able to confirm that our protocol indeed produces entanglement for both homogeneous and inhomogeneous systems. Last, we analyze the feasibility of our protocol in several current experimental platforms.

The performance of a quantum processor depends on the characteristics of the device and the quality of the control pulses. Characterizing cloud-based quantum computers and calibrating the pulses that control them is necessary for high-fidelity operations. However, this time-intensive task eats into the availability of the device. Here, we show restless measurements with a dynamic repetition rate that speed-up calibration and characterization tasks. Randomized benchmarking is performed 5.3 times faster on the quantum device than when an active reset is used and without discarding any data. In addition, we calibrate a qubit with parameter scans and error-amplifying gate sequences and show speed-ups of up to a factor of 40 on the quantum device over active reset. Finally, we present a methodology to perform restless quantum process tomography that mitigates restless state preparation errors. These results reduce the footprint of characterization and calibration tasks. Quantum computers can thus either spend more time running applications or run calibrations more often to maintain gate fidelity.

Metasurfaces (MSs) have been utilized to manipulate different properties of electromagnetic waves. By combining local control over the wave amplitude, phase, and polarization into a single tunable structure, a multifunctional and reconfigurable metasurface can be realized, capable of full control over incident radiation. Here, we experimentally validate a multifunctional metasurface architecture for the microwave regime, where variable loads are connected behind the back plane to reconfigurably shape the complex surface impedance. As a proof-of-concept step, we fabricate several metasurface instances with static loads in different configurations (surface mount capacitors and resistors of different values in different connection topologies) to validate the approach and showcase the different achievable functionalities. Specifically, we show perfect absorption for oblique incidence (both polarizations), broadband linear-polarization conversion, and beam splitting, demonstrating control over the amplitude, polarization state, and wave front, respectively. Measurements are performed in the 4–18-GHz range inside an anechoic chamber and show good agreement with theoretically anticipated results. Our results clearly demonstrate the practical potential of the proposed architecture for reconfigurable electromagnetic wave manipulation.

Artificial spin ices (ASIs) have traditionally been designed such that each nanomagnet possesses a single-domain magnetic configuration that is assumed to be minimally perturbed by interisland dipolar interactions. Using x-ray photoemission electron microscopy to perform magnetic domain imaging, we study thermally demagnetized ${\mathrm{La}}_{0.7}{\mathrm{Sr}}_{0.3}{\mathrm{Mn}\mathrm{O}}_3$-based brickwork ASI arrays and showed that complex spin textures (CSTs) can be stabilized through an appropriate selection of nanoisland width and interisland spacing. While the width dependence can be explained through the dominance of shape anisotropy in isolated nanoislands, the ASIs we investigate demonstrate a complex dependence on both the nanoisland width and interisland spacing. Micromagnetic simulations reveal that interisland dipolar interactions play a role in the formation of CSTs, which are composed of single- and double-vortex states. Energy analysis of the simulations provides an understanding of the system energetics that arises from a delicate balance between intraisland effects (i.e., shape anisotropy and exchange energy) and interisland effects (i.e., dipolar interactions between nearest-neighbor nanoislands).

In Si quantum dots, the valley degree of freedom, in particular the generally small valley splitting and the dot-dependent valley-orbit phase, adds complexities to the low-energy electron dynamics. In particular, tunnel coupling, a key knob in charge and spin qubit manipulations, is strongly dependent on the valley-orbit coupling in a Si double dot. Here we propose a four-level model that incorporates excited valley states to extract ground-state tunnel coupling information for a Si double quantum dot. This scheme is based on a charge-sensing measurement on a double dot in equilibrium, as proposed in the widely used protocol for a GaAs double dot [DiCarlo et al., Phys. Rev. Lett. 92, 226801 (2004)]. Our theory helps determine both intra- and intervalley tunnel coupling with high accuracy, and is robust against uncertainties in system parameters such as valley splittings in the individual quantum dots.

Quantum key distribution (QKD) provides a methodology for secure key distribution based on fundamental physical laws. Although significant experimental progress has been made, the previous QKD systems are mainly composed of discrete optical and electronic devices. Here, we present a prototype of a QKD transmitter by developing the technologies of integrated silicon photonics and electronics. The photonic chip integrates the essential encoding components for the decoy-state BB84 protocol using polarization encoding, while the electronic chips integrate the dedicated driving circuits. On the basis of these integrated chips, we build a desktop QKD system to verify their performance. Successful QKD experiments are conducted at a repetition frequency of 312.5 MHz using standard fiber and emulated channel loss and the intrinsic quantum bit error rate of the system is as low as 0.41%. The finite-key secret rate is 42.7 kbit/s for the 100-km fiber and 295.5 bit/s for the 40-dB emulated loss. Our results demonstrate the feasibility of QKD based on integrated optoelectronic devices and hopefully take a fundamental toward a system-in-a-package solution.

In the past decade, optical-vortex knots with axial dimensions on the order of ${10}^2$–${10}^5$ wavelengths ($\lambda$) have been implemented in the laboratory. However, many potential applications require a drastic reduction on the size of these knots to the order of $\lambda$. In this work, we show an experimental realization of an optical-vortex knot (trefoil) with an axial length of $1.99\lambda$ contained within a volume ($x\times y\times z$) of $1.90\times 2.19\times 1.99{\lambda }^3$. Prior to focusing, the laser light is linearly polarized ($x$, horizontal) and we observe the knot in the dominant $x$-polarization component of the tightly focused field. We also observe the transition of the trefoil knot to a couple of vortex loops via reconnection of the vortex lines as we change the spatial scale of the angular-spectrum representation of the field.

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

Inspired by the high-speed camera experiments of YouTuber Destin Sandlin (SmarterEveryDay) [D. Sandlin, Dominoes – hardcore mode (2017), [Online; accessed 15-Jul-2021].] on the toppling speed of dominoes over different surfaces, we performed discrete-element simulations of this process, varying the spacing between adjacent and evenly spaced blocks (dominoes). We also varied the block-block and block-surface friction coefficients over a wide range of values to have a complete picture of the behavior of these cooperative, dissipative mechanical systems. We found that a steady wavefront speed $v$ exists for a specific interval of spacings between dominoes and coefficients of friction. Surprisingly, while $v$ is more affected by the domino-domino friction, the domino-surface friction determines whether or not toppling anomalies can appear and stop the wave. Finally, our observations led us to propose a scaling law that is able to predict $v$ based on the domino configuration and friction coefficients, and to correctly reproduce experimental tests.

We study quantum secure direct communication using a general preshared quantum state and a generalization of dense coding. In this scenario, Alice is allowed to apply a unitary operation on the preshared state to encode her message, and the set of allowed unitaries forms a group. To decode the message, Bob is allowed to apply a measurement across his system and the system he receives. In the worst scenario, we guarantee that Eve obtains no information for the message even when Eve accesses the joint system between the system that she intercepts and her original system of the preshared state. For a practical application, we propose a concrete protocol and derive an upper bound of information leakage in the finite-length setting. We also discuss how to apply our scenario to the case with discrete Weyl-Heisenberg representation when the preshared state is unknown.

Properties of time-periodic Hamiltonians can be exploited to increase the dephasing time of qubits and to design protected one- and two-qubit gates. Recently, Huang et al. [Phys. Rev. Applied 15, 034065 (2021)] have shown that Floquet states offer a manifold of working points with dynamical protection larger than the few, usual, static sweet spots. Here, we show how Floquet theory, often used on systems with a single drive tone, can be used to describe approaches to robustly control Floquet qubits in the presence of multiple commensurate drive tones. Using this formulation, we introduce a longitudinal readout protocol to measure the Floquet qubit without the need of first adiabatically mapping the Floquet states back to the static qubit states, resulting in a significant speedup in the measurement time of the Floquet qubit. The analytical approach developed here can be applied to any Hamiltonian involving a small number of distinct drive tones, typical in the study of standard parametric gates for qubits outside of the rotating-wave approximation.