arXiv subjects

Yangyang Ge

Publications and source records attributed to Yangyang Ge.

5 recordsLinked to original sources

arXiv2025

Information-Scrambling-Enhanced Quantum Sensing Beyond the Standard Quantum Limit

Quantum sensing promises measurement precision beyond classical limits, but its practical realization is often hindered by decoherence and the challenges of generating and stabilizing entanglement in large-scale systems. Here, we experimentally demonstrate a scalable, scrambling-enhanced quantum sensing protocol, referred to as butterfly metrology, implemented on a cross-shaped superconducting quantum processor. By harnessing quantum information scrambling, the protocol converts local interactions into delocalized metrologically useful correlations, enabling robust signal amplification through interference of the scrambled and polarized quantum states. We validate the time-reversal ability via Loschmidt echo measurements and quantify the information scrambling through out-of-time-ordered correlators, establishing the essential quantum resources of our protocol. Our measurements reveal that the sensing sensitivity surpasses the standard quantum limit (SQL) with increasing qubit number, reaching 3.78 in a 9-qubit configuration, compared to the SQL of 3.0. The scheme further exhibits inherent robustness to coherent control errors and probed signal noise. This work demonstrates a readily scalable path toward practical quantum sensing advantages with prevalent experimental platforms.

quant-ph↗

arXiv2025

Efficient Implementation of a Single-Qutrit Gate Set via Coherent Control

Qutrits offer the potential for enhanced quantum computation by exploiting an enlarged Hilbert space. However, the synthesis of high-fidelity and fast qutrit gates, particularly for single qutrits, remains an ongoing challenge, as it involves overcoming intrinsic constraints in quantum platforms. Here, we develop a novel framework for the efficient implementation of a single-qutrit gate set via coherent control, leveraging SU(3) dynamics while obviating platform-specific constraints such as those arising from the selection rule. As a proof-of-principle demonstration, we realize 35-ns qutrit Hadamard and X gates using a superconducting transmon, achieving an average fidelity of 99.5\%, as verified by randomized benchmarking. We further demonstrate two paradigmatic quantum circuits, which can be naturally extended to scalable qudit algorithms for phase estimation and parity check. In addition, we propose an SU(3)-based decomposition strategy for an arbitrary single-qutrit gate and numerically demonstrate its substantial efficiency improvement over conventional SU(2)-based protocols. By addressing the challenge of efficiently implementing single-qutrit gates, our protocol paves the way for realizing high-performance qutrit processors in diverse quantum platforms.

quant-ph↗

arXiv2024

Long-Range $ZZ$ Interaction via Resonator-Induced Phase in Superconducting Qubits

Superconducting quantum computing emerges as one of leading candidates for achieving quantum advantage. However, a prevailing challenge is the coding overhead due to limited quantum connectivity, constrained by nearest-neighbor coupling among superconducting qubits. Here, we propose a novel multimode coupling scheme using three resonators driven by two microwaves, based on the resonator-induced phase gate, to extend the $ZZ$ interaction distance between qubits. We demonstrate a CZ gate fidelity exceeding 99.9\% within 160 ns at free spectral range (FSR) of 1.4 GHz, and by optimizing driving pulses, we further reduce the residual photon to nearly $10^{-3}$ within 100 ns at FSR of 0.2 GHz. These facilitate the long-range CZ gate over separations reaching sub-meters, thus significantly enhancing qubit connectivity and making a practical step towards the scalable integration and modularization of quantum processors. Specifically, our approach supports the implementation of quantum error correction codes requiring high connectivity, such as low-density parity check codes that paves the way to achieving fault-tolerant quantum computing.

quant-ph↗

arXiv2019

Towards Personalized Management of Type B Aortic Dissection Using STENT: a STandard cta database with annotation of the ENtire aorta and True-false lumen

Type B Aortic Dissection(TBAD) is a rare aortic disease with a high 5-year mortality.Personalized and precise management of TBAD has been increasingly desired in clinic which requires the geometric parameters of TBAD specific to the patient be measured accurately.This remains to be a challenging task for vascular surgeons as manual measurement is highly subjective and imprecise. To solve this problem,we introduce STENT-a STandard cta database with annotation of the ENtire aorta and True-false lumen. The database contains 274 CT angiography (CTA) scans from 274 unique TBAD patients and is split into a training set(254 cases including 210 preoperative and 44 postoperative scans ) and a test set(20 cases).Based on STENT,we develop a series of methods including automated TBAD segmentation and automated measurement of TBAD parameters that facilitate personalized and precise management of the disease. In this work, the database and the proposed methods are thoroughly introduced and evaluated and the results of our study shows the feasibility and effectiveness of our approach to easing the decision-making process for vascular surgeons during personalized TBAD management.

physics.med-ph↗

arXiv2018

Multi-Task Deep Convolutional Neural Network for the Segmentation of Type B Aortic Dissection

Segmentation of the entire aorta and true-false lumen is crucial to inform plan and follow-up for endovascular repair of the rare yet life threatening type B aortic dissection. Manual segmentation by slice is time-consuming and requires expertise, while current computer-aided methods focus on the segmentation of the entire aorta, are unable to concurrently segment true-false lumen, and some require human interaction. We here report a fully automated approach based on a 3-D multi-task deep convolutional neural network that segments the entire aorta and true-false lumen from CTA images in a unified framework. For training, we built a database containing 254 CTA images (210 preoperative and 44 postoperative) obtained using various systems from 254 unique patients with type B aortic dissection. Slice-wise manual segmentation of the entire aorta and the true-false lumen for each 3-D CTA image was provided. Upon evaluation of another 16 CTA images (11 preoperative and 5 postoperative) with ground truth segmentation provided by experienced vascular surgeons, our method achieves a mean dice similarity score(DSC) of 0.910,0.849 and 0.821 for the entire aorta,true lumen and false lumen respectively.

cs.CV↗