arXiv subjects

Xiao Geng

Publications and source records attributed to Xiao Geng.

3 recordsLinked to original sources

arXiv2026

Oval-shaped resonance distortion as a signature of quasiparticle heating effect in a niobium superconducting resonator

We investigate the nonlinear behavior of a superconducting microwave resonator subjected to a dissipative mechanism where the associated quality factor (Q factor) decreases with increasing dissipated power, leading to a dissipative feedback effect. By modifying the Rothwarf-Taylor equations, we establish a macroscopic quasiparticle heating (QPH) model that directly links the quality factor to the microwave readout power. The key finding is the identification of a distinctive oval-shaped distortion in the resonance circle in the complex plane. This distortion serves as a practical experimental signature for identifying the readout power regime in which QPH dominates the loss, under conditions where other nonlinear mechanisms are sufficiently weak. To validate the model, we design and fabricate a niobium (Nb) half-wavelength coplanar waveguide (CPW) resonator and conduct systematic bath temperature and readout power sweeps. The model provides a well fit to the observed oval-shaped resonance circle distortion across a wide range of operating conditions, confirming the QPH mechanism as the primary source of the dissipative non-linearity in the parameter space investigated.

cond-mat.supr-con↗

arXiv2023

Qubit Energy Tuner Based on Single Flux Quantum Circuits

A device called qubit energy tuner (QET) based on single flux quantum (SFQ) circuits is proposed for Z control of superconducting qubits. Created from the improvement of flux digital-to-analog converters (flux DACs), a QET is able to set the energy levels or the frequencies of qubits, especially flux-tunable transmons, and perform gate operations requiring Z control. The circuit structure of QET is elucidated, which consists of an inductor loop and flux bias units for coarse tuning or fine tuning. The key feature of a QET is analyzed to understand how SFQ pulses change the inductor loop current, which provides external flux for qubits. To verify the functionality of the QET, three simulations are carried out. The first one verifies the responses of the inductor loop current to SFQ pulses. The results show that there is about 4.2% relative deviation between analytical solutions of the inductor loop current and the solutions from WRSpice time-domain simulation. The second and the third simulations with QuTip show how a Z gate and an iSWAP gate can be performed by this QET, respectively, with corresponding fidelities 99.99884% and 99.93906% for only once gate operation to specific initial states. These simulations indicate that the SFQ-based QET could act as an efficient component of SFQ-based quantum-classical interfaces for digital Z control of large-scale superconducting quantum computers.

quant-ph↗

arXiv2022

Deep Learning Framework for Multi-Round Service Bundle Recommendation in Iterative Mashup Development

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service-oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single-round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR can outperform several state-of-the-art service recommendation methods to develop new mashups iteratively.

cs.SE↗