Variational quantum algorithm for node embedding

Quantum machine learning has made remarkable progress in many important tasks.However,the gate complexity of the initial state preparation is seldom considered in lots of quantum machine learning algorithms,making them non-end-to-end.Herein,we propose a quantum algorithm for the node embedding problem that maps a node graph's topological structure to embedding vectors.The resulting quantum embedding state can be used as an input for other quantum machine learning algorithms.With O(log(N))qubits to store the information of N nodes,our algorithm will not lose quantum advantage for the subsequent quantum information processing.Moreover,owing to the use of a parameterized quantum circuit with O(poly(log(N)))depth,the resulting state can serve as an efficient quantum database.In addition,we explored the measurement complexity of the quantum node embedding algorithm,which is the main issue in training parameters,and extended the algorithm to capture high-order neighborhood information between nodes.Finally,we experimentally demonstrated our algorithm on an nuclear magnetic resonance quantum processor to solve a graph model.

Deep-learning based on-chip rapid spectral imaging with high spatial resolution

Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad ap-plication prospects.Conventional spectral cameras based on scanning methods suffer from the drawbacks of low acquisition speed and large volume.On-chip computational spectral imaging based on metasur-face filters provides a promising scheme for portable applications,but endures long computation time due to point-by-point iterative spec-tral reconstruction and mosaic effect in the reconstructed spectral im-ages.In this study,on-chip rapid spectral imaging was demonstrated,which eliminated the mosaic effect in the spectral image by deep-learning-based spectral data cube reconstruction.The experimental results show that 4 orders of magnitude faster than the iterative spec-tral reconstruction were achieved,and the fidelity of the spectral re-construction for the standard color plate was over 99%for a standard color board.In particular,video-rate spectral imaging was demon-strated for moving objects and outdoor driving scenes with good per-formance for recognizing metamerism,where the concolorous sky and white cars can be distinguished via their spectra,showing great po-tential for autonomous driving and other practical applications in the field of intelligent perception.

A gap closing gap

The intrinsic magnetic topological insulator MnBi_(2)Te_(4) is believed to be a feasible pathway to high temperature quantum anomalous Hall(QAH) effect for its large magnetically induced energy gap(-50 meV) at its Dirac surface states predicted in theory [1]. The quantized anomalous Hall resistance has been achieved in its thin flakes [2] and films[3], though in most cases a magnetic field of several teslas is still required. The expected large surface state gap, however.

A detail-enhanced sampling strategy in Hadamard single-pixel imaging

Hadamard single-pixel imaging is an appealing imaging technique due to its features of low hardware complexity and industrial cost.To improve imaging efficiency,many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples.In this study,we propose an efficient Hadamard basis sampling strategy that employs an exponential probability function to sample Hadamard patterns in a direction with high energy concentration of the Hadamard spectrum.We used the compressed-sensing algorithm for image reconstruction.The simulation and experimental results show that this sampling strategy can reconstruct object reliably and preserves the edge and details of images.