arXiv subjects

Yifei Cui

Publications and source records attributed to Yifei Cui.

3 recordsLinked to original sources

arXiv2026

PAEMS: Precise and Adaptive Error Model for Superconducting Quantum Processors

Superconducting quantum processor units (QPUs) are incapable of producing massive datasets for quantum error correction (QEC) because of hardware limitations. Thus, QEC decoders heavily depend on synthetic data from qubit error models. Classic depolarizing error models with polynomial complexity present limited accuracy. Coherent density matrix methods suffer from exponential complexity $\propto O(4^n)$ where $n$ represents the number of qubits. This paper introduces PAEMS: a precise and adaptive qubit error model. Its qubit-wise separation framework, incorporating leakage propagation, captures error evolvements crossing spatial and temporal domains. Utilizing repetition-code experiment datasets, PAEMS effectively identifies the intrinsic qubit errors through an end-to-end optimization pipeline. Experiments on IBM's QPUs have demonstrated a 19.5$\times$, 9.3$\times$, and 5.2$\times$ reduction in timelike, spacelike, and spacetime error correlation, respectively, surpassing all of the previous works. It also outperforms the accuracy of Google's SI1000 error model by 58$\sim$73\% on multiple quantum platforms, including IBM's Brisbane, Sherbrooke, and Torino, as well as China Mobile's Wuyue and China Telecom's Tianyan.

quant-ph↗

arXiv2025

Effects of particle elongation on dense granular flows down a rough inclined plane

Granular materials in nature are nearly always non-spherical, but particle shape effects in granular flow remain largely elusive. This study uses discrete element method simulations to investigate how elongated particle shapes affect the mobility of dense granular flows down a rough incline. For a range of systematically varied particle length-to-diameter aspect ratios (AR), we run simulations with various flow thicknesses $h$ and slope angles $\theta$ to extract the well-known $h_\textrm{stop}(\theta)$ curves (below which the flow ceases) and the $Fr$-$h/h_\textrm{stop}$ relations following Pouliquen's approach, where $Fr=u/\sqrt{gh}$ is the Froude number, $u$ is the mean flow velocity, and $g$ is the gravitational acceleration. The slope $\beta$ of the $Fr$-$h/h_\textrm{stop}$ relations shows an intriguing S-shaped dependence on AR, with two plateaus at small and large AR, respectively, transitioning with a sharp increase. We understand this S-shaped dependence by examining statistics of particle orientation, alignment, and hindered rotation. We find that the rotation ability of weakly elongated particles ($\textrm{AR}\lesssim1.3$) remains similar to spheres, leading to the first plateau in the $\beta$-AR relation, whereas the effects of particle orientation saturates beyond $\textrm{AR}\approx2.0$, explaining the second plateau. An empirical sigmoidal function is proposed to capture this non-linear dependence. The findings are expected to enhance our understanding of how particle shape affects the flow of granular materials from both the flow- and particle-scale perspectives.

cond-mat.soft↗

arXiv2024

High-Level Surface Code Decoding via Parallel FFNNs on CIM Platforms

Due to the high sensitivity of qubits to environmental noise, which leads to decoherence and information loss, active quantum error correction(QEC) is essential. Surface codes represent one of the most promising fault-tolerant QEC schemes, but they require decoders that are accurate, fast, and scalable to large-scale quantum platforms. In all types of decoders, fully neural network-based high-level decoders offer decoding thresholds that surpass baseline decoder-Minimum Weight Perfect Matching (MWPM), and exhibit strong scalability, making them one of the ideal solutions for addressing surface code challenges. However, current fully neural network-based high-level decoders can only operate serially and do not meet the current latency requirements (below 440 ns). To address these challenges, we first propose a parallel fully feedforward neural network (FFNN) high-level surface code decoder, and comprehensively measure its decoding performance on a computing-in-memory (CIM) hardware simulation platform. With the currently available hardware specifications, our work achieves a decoding threshold of 14.22%, surpassing the MWPM baseline of 10.3%, and achieves high pseudo-thresholds of 10.4%, 11.3%, 12%, and 11.6% with decoding latencies of 197.03 ns, 234.87 ns, 243.73 ns, and 251.65 ns for distances of 3, 5, 7 and 9, respectively. The impact of hardware parameters and non-idealities on these results is discussed, and the hardware simulation results are extrapolated to a 4K quantum cryogenic environment.

cs.AR↗