arXiv subjects

Shuyi Pan

Publications and source records attributed to Shuyi Pan.

4 recordsLinked to original sources

arXiv2026

A scalable edge-pass Purcell filter for high-fidelity readout of superconducting qubits

High-fidelity readout with strong Purcell protection of qubit coherence is essential for scalable superconducting quantum processors, yet the finite passband and sizable footprint of conventional band-pass Purcell filters make them hard to scale. Here we introduce a scalable edge-pass Purcell filter that separates the readout band from the protected qubit band by a single transmission edge, freeing the readout resonators from bandwidth constraint. Depending on whether the transmitting band lies above or below the cutoff, the compact network is realized as a high-pass filter (HPF) or a low-pass filter (LPF). The HPF reaches an average readout fidelity of 99.46(4)% (up to 99.56%) with a 150-ns pulse, and the LPF reaches 99.49(3)% (up to 99.57%) with a 130-ns pulse. The average single-qubit gate fidelities are 99.94% (HPF) and 99.93% (LPF). Relative to the filter-free Purcell limit, the filters substantially extend the qubit lifetime, and the Purcell protection deepens at higher filter order. In addition, an intrinsic dissipation mode of the filter offers a qubit-reset channel. This leads to a compact architecture that unifies fast, high-fidelity readout, Purcell protection, and effective reset within a single filter for large-scale fault-tolerant quantum computation.

quant-ph↗

arXiv2026

Multi-Resolution Voxelized Map-Based Stereo Visual-Inertial Odometry

Incorporating prior maps significantly enhances the accuracy and robustness of pose estimation in visual-inertial odometry (VIO). However, the large data volume of such maps, combined with limited transmission bandwidth, makes it impractical to continuously load local maps onto an edge device. In this paper, we propose a multi-resolution prior map construction method and a corresponding map-based VIO system. The prior map is voxelized at multiple resolutions, with each voxel retaining only a single map point. During online VIO operation, a cone-shaped indexing strategy associates 2D features on the edge device with 3D map points. The cone's intercept is determined by the distance from the current position to the 3D points, enabling the selection of the appropriate resolution level and the retrieval of the unique map point within the corresponding voxel via a 3D digital differential analyzer (DDA) algorithm. This approach minimizes both the volume of data required for transmission and the computational load during data association. Extensive experiments on two public datasets demonstrate that our system achieves accurate pose estimation while requiring minimal data transmission.

cs.RO↗

arXiv2026

Breaking the scalability barrier via a vertical tunable coupler in 3D integrated transmon system

Scaling superconducting quantum processors beyond the constraints of monolithic planar architectures is essential for fault-tolerant quantum computation. Here we demonstrate a three-dimensional (3D) integrated superconducting quantum processor in which two qubit chips are vertically stacked on opposing sides of a carrier chip and galvanically connected via multilayer flip-chip bonding. Intrachip qubit coupling is mediated by planar tunable couplers, whereas interchip coupling is enabled by vertical tunable couplers embedded in the carrier chip. Randomized benchmarking reveals simultaneous single-qubit gate fidelities of 99.87 % with negligible crosstalk, and controlled-Z gates achieve an average fidelity of 97.5 % for both intrachip and interchip operations. We further demonstrate high-fidelity Bell-state preparation and coherent generation of a four-qubit $W$ state, confirming the architecture's capability for interchip entanglement distribution. These results establish vertical coupling as a promising pathway toward scalable quantum processors compatible with advanced quantum error-correcting codes.

quant-ph↗

arXiv2026

Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing

Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random nature of exercise recommendations and student choices inevitably induces severe selection bias. Most existing KT methods neglect this issue, training on observed logs using standard empirical risk, which yields biased mastery estimates and accumulates errors in subsequent recommendations. To address this, we introduce a doubly robust (DR) formulation for KT that integrates a propensity model with an error imputation model, theoretically guaranteeing unbiasedness if either model is accurate. Beyond unbiasedness, in the sequential setting of KT, we identify that the estimator's performance is compromised by variance-dependent stochastic deviations that accumulate over time, thereby causing training instability and limiting performance. To mitigate this, we derive a generalization bound that explicitly characterizes the impact of estimator variance and identifies temporal smoothness as a key factor in controlling it. Building on these theoretical insights, we propose the Temporal Smoothness Doubly Robust (TSDR) framework. TSDR jointly optimizes the KT predictor and the imputation model with a smoothness regularizer, effectively reducing variance while preserving the unbiasedness guarantee of DR. Experiments on multiple real-world benchmarks demonstrate that TSDR consistently enhances various state-of-the-art KT backbones, underscoring the vital role of principled bias correction in KT.

cs.AI↗