arXiv subjects

Haochen Shen

Publications and source records attributed to Haochen Shen.

4 recordsLinked to original sources

arXiv2026

Anomalous Microwave Response in YBCO Resonators beyond the Two-Level-System Model

We report the microwave response of coplanar-waveguide (CPW) resonators fabricated from $\mathrm{YBa_2Cu_3O_{7-\delta}}$ (YBCO) thin films over temperatures from approximately $70~\mathrm{mK}$ to $40~\mathrm{K}$. The resonators exhibit internal quality factors $Q_\mathrm{i}$ in the range of $4\times10^3$ to $10^4$ at 70 mK, which increase to a maximum of approximately $8\times10^3$ to $1.2\times10^4$ near $6~\mathrm{K}$. At low temperatures, both $Q_\mathrm{i}$ and the fractional shift of the resonance frequency $\Delta f_\mathrm{r}/f_\mathrm{r}$ increases with temperature, qualitatively resembling behavior commonly associated with two-level-system (TLS) defects. However, neither response saturates on the temperature scale set by the resonator frequency, and the loss exhibits no observable microwave-power dependence. We show that low-temperature frequency upturn may be better described by an additional paramagnetic response associated with defect-induced local moments or Andreev bound states, while the low-temperature loss follows an approximately logarithmic temperature dependence whose microscopic origin remains unresolved. These measurements establish the millikelvin performance of patterned YBCO resonators and show that their low-temperature response cannot be understood within the conventional TLS framework alone.

cond-mat.supr-con↗

arXiv2026

A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond

Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.

quant-ph↗

arXiv2025

Simultaneous Determination of Local Magnetic Fields and Sensor Orientation with Nitrogen-Vacancy Centers in Nanodiamond

Nitrogen-vacancy (NV) centers in nanodiamonds have emerged as a promising quantum sensing platform for biomedical imaging applications, yet random orientations of individual particles present significant challenges in large-scale sensor calibration. In this study, we demonstrate a novel approach to simultaneously determine each particle's crystallographic axes and the surrounding local vector magnetic field. Specifically, a minimum of four distinct bias fields is required to unambiguously extract both the orientation and the local field. We validate our method experimentally using NV centers in two scenarios: (1) in a bulk diamond with known crystal orientation as a proof of concept, and (2) on various single nanodiamonds to mimic real-world applications. Our work represents a crucial step towards unlocking the full potential of nanodiamonds for advanced applications such as in-situ biomedical imaging and nanoscale sensing in complex environments.

quant-ph↗

arXiv2022

AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.

cs.CV↗