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A wafer-scale graphene circuit was demonstrated in which all circuit components, including graphene field-effect transistor and inductors, were monolithically integrated on a single silicon carbide wafer. The integrated circuit operates as a broadband radio-frequency mixer at frequencies up to 10 gigahertz. These graphene circuits exhibit outstanding thermal stability with little reduction in performance (less than 1 decibel) between 300 and 400 kelvin. These results open up possibilities of achieving practical graphene technology with more complex functionality and performance.

The controlled motion of a series of domain walls along magnetic nanowires using spin-polarized current pulses is the essential ingredient of the proposed magnetic racetrack memory, a new class of potential non-volatile storage-class memories. Using permalloy nanowires, we achieved the successive creation, motion, and detection of domain walls by using sequences of properly timed, nanosecond-long, spin-polarized current pulses. The cycle time for the writing and shifting of the domain walls was a few tens of nanoseconds. Our results illustrate the basic concept of a magnetic shift register that relies on the phenomenon of spin-momentum transfer to move series of closely spaced domain walls.

Efficient signal communication uses photons. Signal processing, however, uses an optically inactive medium, electrons. Therefore, an interconnection between electronic signal processing and optical communication is required at the integrated circuit level. We demonstrated control of exciton fluxes in an excitonic integrated circuit. The circuit consists of three exciton optoelectronic transistors and performs operations with exciton fluxes, such as directional switching and merging. Photons transform into excitons at the circuit input, and the excitons transform into photons at the circuit output. The exciton flux from the input to the output is controlled by a pattern of the electrode voltages. The direct coupling of photons, used in communication, to excitons, used as the device-operation medium, may lead to the development of efficient exciton-based optoelectronic devices.

A passive optical diode effect would be useful for on-chip optical information processing but has been difficult to achieve. Using a method based on optical nonlinearity, we demonstrate a forward-backward transmission ratio of up to 28 decibels within telecommunication wavelengths. Our device, which uses two silicon rings 5 micrometers in radius, is passive yet maintains optical nonreciprocity for a broad range of input power levels, and it performs equally well even if the backward input power is higher than the forward input. The silicon optical diode is ultracompact and is compatible with current complementary metal-oxide semiconductor processing.

Cavity quantum electrodynamics: Fock states represent quantum purity In cavity quantum electrodynamics (QED), light–matter interactions between a single emitter (an atom or an atom-like system with discrete energy levels) and a resonant optical cavity are investigated at a fundamental level. Recent advances in solid-state implementations, which offer great design flexibility, have given this field considerable momentum. An outstanding important question has been which features in such a system show true quantum behaviour and cannot be explained with classical models. Hofheinz et al . study a 'circuit' QED system where a superconducting qubit acts as an atom-like two-energy level system and is embedded in a microwave transmission circuit, acting as the optical cavity. They demonstrate in this system the creation of pure quantum states, known as Fock states, which give specific numbers of energy quanta, in this case photons. Fock states with up to six photons are prepared and analysed. The results are important because cavity QED is expected to play a crucial role in the development of quantum information processing and communication applications. A 'circuit' quantum electrodynamics system where a superconducting qubit acts as an atom-like two-energy level system and is embedded in a microwave transmission circuit (acting as the optical cavity) is studied. In this system, it is demonstrated that the creation of pure quantum states, known as Fock states, which give specific numbers of energy quanta, in this case photons. Fock states with up to six photons are prepared and analysed. Spin systems and harmonic oscillators comprise two archetypes in quantum mechanics 1 . The spin-1/2 system, with two quantum energy levels, is essentially the most nonlinear system found in nature, whereas the harmonic oscillator represents the most linear, with an infinite number of evenly spaced quantum levels. A significant difference between these systems is that a two-level spin can be prepared in an arbitrary quantum state using classical excitations, whereas classical excitations applied to an oscillator generate a coherent state, nearly indistinguishable from a classical state 2 . Quantum behaviour in an oscillator is most obvious in Fock states, which are states with specific numbers of energy quanta, but such states are hard to create 3 , 4 , 5 , 6 , 7 . Here we demonstrate the controlled generation of multi-photon Fock states in a solid-state system. We use a superconducting phase qubit 8 , which is a close approximation to a two-level spin system, coupled to a microwave resonator, which acts as a harmonic oscillator, to prepare and analyse pure Fock states with up to six photons. We contrast the Fock states with coherent states generated using classical pulses applied directly to the resonator.

Magnetoelastic effect characterizes the change of materials’ magnetic properties under mechanical deformation, which is conventionally observed in some rigid metals or metal alloys. Here we show magnetoelastic effect can also exist in 1D soft fibers with stronger magnetomechanical coupling than that in traditional rigid counterparts. This effect is explained by a wavy chain model based on the magnetic dipole-dipole interaction and demagnetizing factor. To facilitate practical applications, we further invented a textile magnetoelastic generator (MEG), weaving the 1D soft fibers with conductive yarns to couple the observed magnetoelastic effect with magnetic induction, which paves a new way for biomechanical-to-electrical energy conversion with short-circuit current density of 0.63 mA cm −2 , internal impedance of 180 Ω, and intrinsic waterproofness. Textile MEG was demonstrated to convert the arterial pulse into electrical signals with a low detection limit of 0.05 kPa,  even with heavy perspiration or in underwater situations without encapsulations. The authors invented a textile magnetoelastic generator, weaving 1D soft fibers with conductive yarns to couple the observed magnetoelastic effect with magnetic induction, which paves a new way for biomechanical-to-electrical energy conversion.

Microfluidic systems are rapidly becoming commonplace tools for high-precision materials synthesis, biochemical sample preparation, and biophysical analysis. Typically, microfluidic systems are constructed in monolithic form by means of microfabrication and, increasingly, by additive techniques. These methods restrict the design and assembly of truly complex systems by placing unnecessary emphasis on complete functional integration of operational elements in a planar environment. Here, we present a solution based on discrete elements that liberates designers to build large-scale microfluidic systems in three dimensions that are modular, diverse, and predictable by simple network analysis techniques. We develop a sample library of standardized components and connectors manufactured using stereolithography. We predict and validate the flow characteristics of these individual components to design and construct a tunable concentration gradient generator with a scalable number of parallel outputs. We show that these systems are rapidly reconfigurable by constructing three variations of a device for generating monodisperse microdroplets in two distinct size regimes and in a high-throughput mode by simple replacement of emulsifier subcircuits. Finally, we demonstrate the capability for active process monitoring by constructing an optical sensing element for detecting water droplets in a fluorocarbon stream and quantifying their size and frequency. By moving away from large-scale integration toward standardized discrete elements, we demonstrate the potential to reduce the practice of designing and assembling complex 3D microfluidic circuits to a methodology comparable to that found in the electronics industry. Significance Microfluidic systems promise to improve the analysis and synthesis of materials, biological or otherwise, by lowering the required volume of fluid samples, offering a tightly controlled fluid-handling environment, and simultaneously integrating various chemical processes. To build these systems, designers depend on microfabrication techniques that restrict them to arranging their designs in two dimensions and completely fabricating their design in a single step. This study introduces modular, reconfigurable components containing fluidic and sensor elements adaptable to many different microfluidic circuits. These elements can be assembled to allow for 3D routing of channels. This assembly approach allows for the application of network analysis techniques like those used in classical electronic circuit design, facilitating the straightforward design of predictable flow systems.

The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

Although universal quantum computers ideally solve problems such as factoring integers exponentially more efficiently than classical machines, the formidable challenges in building such devices motivate the demonstration of simpler, problem-specific algorithms that still promise a quantum speedup. We constructed a quantum boson-sampling machine (QBSM) to sample the output distribution resulting from the nonclassical interference of photons in an integrated photonic circuit, a problem thought to be exponentially hard to solve classically. Unlike universal quantum computation, boson sampling merely requires indistinguishable photons, linear state evolution, and detectors. We benchmarked our QBSM with three and four photons and analyzed sources of sampling inaccuracy. Scaling up to larger devices could offer the first definitive quantum-enhanced computation.