Nature-inspired magnetically responsive intelligent topography surfaces have attracted considerable attention owing to their controllable droplet manipulation abilities. However, it is still challenging for magnetically responsive surfaces to realize three-dimensional (3D) droplet/multidroplet transport in both horizontal and vertical directions. Additionally, the droplet horizontal propulsion speed needs to be improved. In this work, a 3D droplet/multidroplet transport strategy based on magnetically responsive microplates array (MMA) actuated by a spatially varying and periodic magnetic field is proposed. The modified superhydrophobic surface can transport droplets rapidly both in horizontal and vertical directions, and it can even realize against-gravity upslope propulsion. The rapid horizontal droplet propulsion (∼58.6 mm/s) is ascribed to the abrupt inversion of the modified surface induced by the specific magnetic field. Furthermore, the nonmagnetically responsive microplates (NMMs)/MMA composite surface is constructed to realize 3D multidroplet manipulation. The implementations of MMA in manipulation of continuous fluids and liquid metal are further demonstrated, providing a valuable platform for microfluidic applications.

Classical simulations of quantum circuits are limited in both space and time when the qubit count is above 50, the realm where quantum supremacy reigns. However, recently, for the low depth circuit with more than 50 qubits, there are several methods of simulation proposed by teams at Google and IBM. Here, we present a scheme of simulation which can extract a large amount of measurement outcomes within a short time, achieving a 64-qubit simulation of a universal random circuit of depth 22 using a 128-node cluster, and 56-and 42-qubit circuits on a single PC. We also estimate that a 72-qubit circuit of depth 23 can be simulated in about 16 h on a supercomputer identical to that used by the IBM team. Moreover, the simulation processes are exceedingly separable, hence parallelizable, involving just a few inter-process communications. Our work enables simulating more qubits with less hardware burden and provides a new perspective for classical simulations.

Natural evolution has endowed diverse species with distinct geometric micro/nanostructures exhibiting admirable functions. Examples include anisotropic microgrooves/microstripes on the rice leaf surface for passive liquid directional rolling, and motile microcilia widely existed in mammals’ body for active matter transportation through in situ oscillation. Till now, bionic studies have been extensively performed by imitating a single specific biologic functional system. However, bionic fabrication of devices integrating multispecies architectures is rarely reported, which may sparkle more fascinating functionalities beyond natural findings. Here, a cross-species design strategy is adopted by combining the anisotropic wettability of the rice leaf surface and the directional transportation characteristics of motile cilia. High-aspect-ratio magnetically responsive microcolumn array (HAR-MRMA) is prepared for active droplet transportation. It is found that just like the motile microcilia, the unidirectional waves are formed by the real-time reconstruction of the microcolumn array under the moving magnetic field, enabling droplet (1–6 μL) to transport along the predetermined anisotropic orbit. Meanwhile, on-demand droplet horizontal transportation on the inclined plane can be realized by the rice leaf-like anisotropic surface, showcasing active nongravity-driven droplet transportation capability of the HAR-MRMA. The directional lossless transportation of droplet holds great potential in the fields of microfluidics, chemical microreaction, and intelligent droplet control system.

The quantum-classical hybrid algorithm is a promising algorithm with respect to demonstrating the quantum advantage in noisy-intermediate-scale quantum (NISQ) devices. When running such algorithms, effects due to quantum noise are inevitable. In our work, we consider a well-known hybrid algorithm, the quantum approximate optimization algorithm (QAOA). We study the effects on QAOA from typical quantum noise channels, and produce several numerical results. Our research indicates that the output state fidelity, i.e., the cost function obtained from QAOA, decreases exponentially with respect to the number of gates and noise strength. Moreover, we find that when noise is not serious, the optimized parameters will not deviate from their ideal values. Our result provides evidence for the effectiveness of hybrid algorithms running on NISQ devices.

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Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

Stimuli-responsive anisotropic slippery surfaces (ASSs) have demonstrated intriguing performance in manipulating the behaviors of some liquids. However, most present methods have been limited to conductive droplets, certain specific conductive platforms, and higher manipulation temperature that greatly hinder its practical applications. Here, an electric-responsive paraffin-infused ASS (ER-PIASS) composed of paraffin, microgrooved PDMS, and flexible embedded silver nanowire heater is reported. Owing to the fast electric-response of ER-PIASS, smart control between anisotropic sliding and pinning for diverse liquids can be realized by remotely loading and discharging electric-stimuli. The underlying mechanism is that the generated Joule heat melts the solidified paraffin to slide a pinning droplet once an electric-trigger is loaded due to the formation of a slippery air/liquid/liquid/solid system. Once the voltage is discharged, the liquefied paraffin would rapidly solidify to stick to a slipping droplet because of the recovery of a frictional air/liquid/solid system. Additionally, the effect of the groove’s height (h), spacing between two adjacent grooves (d), and thickness of the paraffin layer on the anisotropic degree was quantitatively studied and an optimized value of 75° is thus harvested. Through tuning the recipe of the hybrid lubricant, the responsive voltage and temperature for ER-PIASS can be dramatically decreased to ultralow figures of 2.0 V and 34.2 °C. By taking advantage of this ultralow-voltage-driven biocompatible ER-PIASS, we enable the anisotropic smart control of cell culture medium and yeast droplets for their directional coalesce, growth, and fission. We believe that such stimuli-responsive surfaces will be promising candidates for manipulating droplets’ directional sliding behavior and further bloom the studies of flexible microfluidics devices.

While quantum computing provides an exponential advantage in solving linear differential equations, there are relatively few quantum algorithms for solving nonlinear differential equations. In our work, based on the homotopy perturbation method, we propose a quantum algorithm for solving n-dimensional nonlinear dissipative ordinary differential equations (ODEs). Our algorithm first converts the original nonlinear ODEs into other nonlinear ODEs which can be embedded into finite-dimensional linear ODEs. Then we solve the embedded linear ODEs with quantum linear ODEs algorithm and obtain a state ε-close to the normalized exact solution of the original nonlinear ODEs with success probability Ω(1). The complexity of our algorithm is O(gηTpoly(log(nT/ε))), where η, g measure the decay of the solution. Our algorithm provides exponential improvement over the best classical algorithms or previous quantum algorithms in n or ε.