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The synthetic Floquet lattice, generated by multiple strong drives with mutually incommensurate frequencies, provides a powerful platform for the quantum simulation of topological phenomena. In this study, we propose a 4-band tight-binding model of the Chern insulator with a Chern number C = $\pm$2 by coupling two layers of the half-BHZ lattice and subsequently mapping it onto the Floquet lattice to simulate its topological properties. To determine the Chern number of our Floquet-version model, we extend the energy pumping method proposed by Martin et al. [Phys. Rev. X 7, 041008 (2017)] and the topological oscillation method introduced by Boyers et al. [Phys. Rev. Lett. 125, 160505 (2020)], followed by numerical simulations for both methodologies. The simulation results demonstrate the successful extraction of the Chern number using either of these methods, providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half-BHZ model. Finally, we briefly discuss a potential experimental implementation for our model. Our work demonstrates significant potential for simulating complex topological matter using quantum computing platforms, thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena.

Recently conducted experiments on two-dimensional topological superconductors have revealed various indications of Majorana zero modes (MZMs). However, progress in the manipulation of MZM braiding has been limited, impeding the realization of topological quantum computing. In this study, we propose a potential braiding scheme based on a spintronic device matrix. This scheme involves utilizing a matrix composed of spin-transfer torque devices (STT-matrix) alongside a two-dimensional topological superconductor material. By programming the ON/OFF states of the spintronic devices within the STT-matrix, it becomes possible to manipulate vortices hosting MZMs in the two-dimensional topological superconductor. To further investigate this concept, we construct a time-dependent Ginzburg-Landau model and perform numerical simulations to analyze vortex-driving dynamics, MZM braiding processes, and MZM fusion phenomena. Our findings demonstrate that this system exhibits high versatility and flexibility in manipulating vortices. With advancements in spintronic device technology, our proposed scheme offers a feasible and practical method for operating MZMs within vortices present in topological superconductors.

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies., Comment: 4 pages, 3 figures

Semantic consistency recognition aims to detect and judge whether the semantics of two text sentences are consistent with each other. However, the existing methods usually encounter the challenges of synonyms, polysemy and difficulty to understand long text. To solve the above problems, this paper proposes a co-driven semantic consistency recognition method based on the fusion of Transformer and HowNet sememes knowledge. Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs. Then, interactive attention calculation is carried out utilizing soft-attention and fusion the knowledge with sememes matrix. Finally, bidirectional long short-term memory network (BiLSTM) is exploited to encode the conceptual semantic information and infer the semantic consistency. Experiments are conducted on two financial text matching datasets (BQ, AFQMC) and a cross-lingual adversarial dataset (PAWSX) for paraphrase identification. Compared with lightweight models including DSSM, MwAN, DRCN, and pre-training models such as ERNIE etc., the proposed model can not only improve the accuracy of semantic consistency recognition effectively (by 2.19%, 5.57% and 6.51% compared with the DSSM, MWAN and DRCN models on the BQ dataset), but also reduce the number of model parameters (to about 16M). In addition, driven by the HowNet sememes knowledge, the proposed method is promising to adapt to scenarios with long text., Comment: 17 pages, 5 figures

The accurate characterization of the spatial potential generated by a planar electrode in a surface-type Paul trap is of great interest. To achieve this, we employ a simple yet highly precise parametric expression to describe the spatial field of a rectangular-shaped electrode. Based on this, an optimization method is introduced to precisely characterize the axial electric field intensity created by the powered electrode and the stray field. In contrast to existing methods, various types of experimental data, such as the equilibrium position of ions in a linear string, equilibrium positions of single trapped ions and trap frequencies, are utilized for potential estimation in order to mitigate systematic errors. This approach offers significant flexibility in voltage settings for data collection, making it particularly well-suited for surface electrode traps where ion probe trapping height may vary with casual voltage settings. In our demonstration, we successfully minimized the discrepancy between experimental observations and model predictions to an impressive extent. The relative errors of secular frequencies were suppressed within $\pm$ 0.5$\%$, and the positional error of ions was limited to less than 1.2 $\mu$m, all surpassing those achieved by existing methodologies., Comment: 10 pages, 10 figures

Recent studies indicated that contamination by adatoms on the surface ion trap can generate contact potential, leading to fluctuations in patch potential. By investigating contamination induced by surface adatoms during a loading process, a direct physical image of the contamination process and the relationship between the capacitance change and the contamination from surface adatoms is examined theoretically and experimentally. From the relationship, the contamination by surface adatoms and the effect of in situ treatment process can be monitored by the capacitance between electrodes in real time. This study is foundational to further research on anomalous heating with practical applications in quantum information processing from surface ion traps., Comment: 8 pages, 5 figures

Scaling up and effective cooling of ions in surface ion trap are central challenges in quantum computing and quantum simulation with trapped ions. In this theoretical study, we propose a versatile surface ion trap. In the manipulation zone of our trap, a symmetric seven-wire geometry enables innate principle-axes rotation of two parallel linear ion chains, which facilitates the cooling of ions along all principle trap axes. To alleviate contaminating the manipulation zone during ion loading, a symmetric five-wire geometry is designed as the loading zone. And a "fork junction" connects the loading and manipulation zones, which also enables the shuttling and reordering of ions. A multi-objective optimization procedure suitable for arbitrary junction designs is described in detail, and we present the corresponding optimal results for the key components of our trap. The proposed versatile trap can be used in the construction of large-scale ion quantum processors. The trap also can be used as the multi-ion-mixer or the efficient ion beam splitter, which has the potential applications in quantum simulation and quantum computing, the research of 2D dimensional ion crystals and the guides of quantum microscope, like an electron beam splitter used for quantum matter-wave optics experiments. Interesting topics involving the spin-spin interactions between two ion chains can also be simulated in our trap.