arXiv subjects

S. Zhao

Publications and source records attributed to S. Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Probabilistic generation of two-mode binomial cat states using cross-Kerr interactions

Superpositions of macroscopically distinct coherent states, or cat states, are a key resource for quantum technologies. In particular, two-mode binomial cat states can give rise to exact quantum error correction in continuous-variable quantum computing. However, their preparation typically requires non-Gaussian initial states, such as Fock or NOON states, which are challenging to realize experimentally. Here, we propose a protocol to generate two-mode binomial cat states where the only requirements are Gaussian initial states in combination with heralded heterodyne measurements. Our approach utilizes cross-Kerr interactions between bosonic modes commonly realizable in superconducting circuit architectures. We show that by adjusting the input state parameters, our protocol remains robust to dissipation under realistic experimental conditions. Our work provides a practical route to realizing multinomial cat states in current experimental platforms without requiring non-Gaussian initial resources, overcoming a key limitation of existing preparation schemes.

quant-ph↗

arXiv2026

Dissipative preparation and stabilization of d-mode multinomial cat states

Engineering dissipation with tailored steady states has become a powerful approach for preparing and stabilizing quantum states. In this framework, engineered dissipative processes continuously steer a system towards desired target states while suppressing unwanted noise. However, extending this idea to multimode systems is challenging and remains largely unexplored, although this class of states is a powerful resource for quantum sensing and quantum information processing applications. Here, we propose a general method to design the required dissipative processes for the generation of multimode cat states in bosonic systems. We show that the engineered dissipation prepares such states from the vacuum with high fidelity and robustly stabilizes them against decoherence. As a result, their lifetime is extended by several orders of magnitude compared to natural decay times, which in turn enhances their applications in quantum techonologies. We specifically focus on the preparation and stabilization of two-mode binomial cat states and discuss a pathway for the implementation in superconducting circuit. However, our scheme can also scale up to arbitrary d-mode multinomial cat states associated to $\mathfrak{su}(d\ge2)$ algebras, and thus, our scalable framework provides a feasible route towards stabilizing compact nonclassical states.

quant-ph↗

arXiv2025

UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation

Ultra-high-definition (UHD) images often suffer from severe degradations such as blur, haze, rain, or low-light conditions, which pose significant challenges for image restoration due to their high resolution and computational demands. In this paper, we propose UHDRes, a novel lightweight dual-domain decoupled spectral modulation framework for UHD image restoration. It explicitly models the amplitude spectrum via lightweight spectrum-domain modulation, while restoring phase implicitly through spatial-domain refinement. We introduce the spatio-spectral fusion mechanism, which first employs a multi-scale context aggregator to extract local and global spatial features, and then performs spectral modulation in a decoupled manner. It explicitly enhances amplitude features in the frequency domain while implicitly restoring phase information through spatial refinement. Additionally, a shared gated feed-forward network is designed to efficiently promote feature interaction through shared-parameter convolutions and adaptive gating mechanisms. Extensive experimental comparisons on five public UHD benchmarks demonstrate that our UHDRes achieves the state-of-the-art restoration performance with only 400K parameters, while significantly reducing inference latency and memory usage. The codes and models are available at https://github.com/Zhao0100/UHDRes.

eess.IV↗

arXiv2024

Determining Absolute Neutrino Mass using Quantum Technologies

Next generation tritium decay experiments to determine the absolute neutrino mass require high-precision measurements of $\beta$-decay electron energies close to the kinematic end point. To achieve this, the development of high phase-space density sources of atomic tritium is required, along with the implementation of methods to control the motion of these atoms to allow extended observation times. A promising approach to efficiently and accurately measure the kinetic energies of individual $\beta$-decay electrons generated in these dilute atomic gases, is to determine the frequency of the cyclotron radiation they emit in a precisely characterised magnetic field. This cyclotron radiation emission spectroscopy technique can benefit from recent developments in quantum technologies. Absolute static-field magnetometry and electrometry, which is essential for the precise determination of the electron kinetic energies from the frequency of their emitted cyclotron radiation, can be performed using atoms in superpositions of circular Rydberg states. Quantum-limited microwave amplifiers will allow precise cyclotron frequency measurements to be made with maximal signal-to-noise ratios and minimal observation times. Exploiting the opportunities offered by quantum technologies in these key areas, represents the core activity of the Quantum Technologies for Neutrino Mass project. Its goal is to develop a new experimental apparatus that can enable a determination of the absolute neutrino mass with a sensitivity on the order of 10~meV/$c^2$.

hep-ex↗

arXiv2024

Edge-based Modeling for Disease Transmission on Random Graphs: An Application to Mitigate a Syphilis Outbreak

Edge-based network models, especially those based on bond percolation methods, can be used to model disease transmission on complex networks and accommodate social heterogeneity while keeping tractability. Here we present an application of an edge-based network model to the spread of syphilis in the Kingston, Frontenac and Lennox & Addington (KFL&A) region of Southeastern Ontario, Canada. We compared the results of using a network-based susceptible-infectious-recovered (SIR) model to those generated from using a traditional mass action SIR model. We found that the network model yields very different predictions, including a much lower estimate of the final epidemic size. We also used the network model to estimate the potential impact of introducing a rapid syphilis point of care test (POCT) and treatment intervention strategy that has recently been implemented by the public health unit to mitigate syphilis transmission.

q-bio.PE↗

arXiv2024

Testing Bayesian inference of GRMHD model parameters from VLBI data

Recent observations by the Event Horizon Telescope (EHT) of supermassive black holes M87* and Sgr A* offer valuable insights into their spacetime properties and astrophysical conditions. Utilizing a library of model images (~2 million for Sgr A*) generated from general-relativistic magnetohydrodynamic (GRMHD) simulations, limited and coarse insights on key parameters such as black hole spin, magnetic flux, inclination angle, and electron temperature were gained. The image orientation and black hole mass estimates were obtained via a scoring and an approximate rescaling procedure. Lifting such approximations, probing the space of parameters continuously, and extending the parameter space of theoretical models is both desirable and computationally prohibitive with existing methods. To address this, we introduce a new Bayesian scheme that adaptively explores the parameter space of ray-traced, GRMHD models. The general relativistic radiative transfer code \ipole is integrated with the EHT parameter estimation tool THEMIS. The pipeline produces a ray-traced model image from GRMHD data, computes predictions for VLBI observables from the image for a specific VLBI array configuration and compares to data thereby sampling the likelihood surface via an MCMC scheme. At this stage we focus on four parameters: accretion rate, electron thermodynamics, inclination, and source position angle. Our scheme faithfully recovers parameters from simulated VLBI data and accommodates time-variabibility via an inflated error budget. We highlight the impact of intrinsic variability on model fitting approaches. This work facilitates more informed inferences from GRMHD simulations and enables expansion of the model parameter space in a statistically robust and computationally efficient manner.

astro-ph.HE↗

arXiv2024

Disease Transmission on Random Graphs Using Edge-Based Percolation

Edge-based percolation methods can be used to analyze disease transmission on complex social networks. This allows us to include complex social heterogeneity in our models while maintaining tractability. Here we review the seminal works on this field by Newman et al (2001); Newman (2002, 2003), and Miller et al (2012). We present a systematic discussion of the theoretical background behind these models, including an extensive derivation of the major results. We also connect these results relate back to the classical literature in random graph theory Molloy and Reed (1995, 1998). Finally, we also present an accompanying R package that takes epidemic and network parameters as input and generates estimates of the epidemic trajectory and final size. This manuscript and the R package was developed to help researchers easily understand and use network models to investigate the interaction between different community structures and disease transmission.

cs.SI↗

arXiv2023

Fast and robust cat state preparation utilizing higher order nonlinearities in Rydberg ensembles

In optical and solid-state architectures, low-order nonlinearities are commonly exploited for quantum state preparation due to their practical accessibility and controllability, while higher-order contributions are typically much weaker and treated as unwanted perturbations. Here, we alternatively show that detuned Rydberg ensembles provide a natural platform where higher-order Kerr nonlinearities can become comparable in strength to lower-orders near multiphoton resonances. We demonstrate that these nonlinearities can be harnessed as a resource for the rapid preparation of non-Gaussian states, with the coexistence of multiple Kerr orders substantially accelerating the evolution of an initial coherent state into Schr\"odinger cat states. Furthermore, by combining the nonlinear dynamics with a controllable linear drive, we can gain the full control over the evolution trajectory, thereby achieving cat-state generation directly from vacuum on timescales beyond the genuine Kerr evolution. Our results establish a paradigm in which naturally occurring higher-order nonlinearities are transformed from unwanted imperfections into a valuable resource for quantum state engineering.

quant-ph↗

arXiv2023

Workshop on a future muon program at FNAL

The Snowmass report on rare processes and precision measurements recommended Mu2e-II and a next generation muon facility at Fermilab (Advanced Muon Facility) as priorities for the frontier. The Workshop on a future muon program at FNAL was held in March 2023 to discuss design studies for Mu2e-II, organizing efforts for the next generation muon facility, and identify synergies with other efforts (e.g., muon collider). Topics included high-power targetry, status of R&D for Mu2e-II, development of compressor rings, FFA and concepts for muon experiments (conversion, decays, muonium and other opportunities) at AMF. This document summarizes the workshop discussions with a focus on future R&D tasks needed to realize these concepts.

hep-ex↗

arXiv2023

A primitive machine learning tool for the mechanical property prediction of multiple principal element alloys

Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising performance due to their exceptional mechanical properties. Training a machine learning (ML) model on known performance data is a reasonable method to rationalise the complexity of composition dependent mechanical properties of MPEAs. This study utilises data from a specifically curated dataset, that contains information regarding six mechanical properties of MPEAs. A parser tool was introduced to convert chemical composition of alloys into the input format of the ML models, and a number of ML models were applied. Finally, Gradio was used to visualise the ML model predictions and to create a user-interactive interface. The ML model presented is an initial primitive model (as it does not factor in aspects such as MPEA production and processing route), however serves as a an initial user tool, whilst also providing a workflow for other researchers.

cond-mat.mtrl-sci↗

arXiv2022

Deeply virtual Compton scattering using a positron beam in Hall-C at Jefferson Lab

We propose to use the High Momentum Spectrometer of Hall C combined with the Neutral Particle Spectrometer (NPS) to perform high precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section using a beam of positrons. The combination of measurements with oppositely charged incident beams is the only unambiguous way to disentangle the contribution of the DVCS$^2$ term in the photon electroproduction cross section from its interference with the Bethe-Heitler amplitude. This provides a stronger way to constrain the Generalized Parton Distributions of the nucleon. A wide range of kinematics accessible with an 11 GeV beam off an unpolarized proton target will be covered. The $Q^2-$dependence of each contribution will be measured independently.

nucl-ex↗

arXiv2021

Double deeply virtual Compton scattering with positron beams at SoLID

Double Deeply Virtual Compton Scattering (DDVCS) is the only experimental channel for the determination of the dependence of the Generalized Parton Distributions (GPDs) on both the average and the transferred momentum independently. The physics observables of the electron induced di-muon production reaction $\vv{e}^{\pm}p \to e^{\pm}pμ^+μ^-$ off unpolarized hydrogen are discussed. Their measurement with the high luminosity and large acceptance SoLID spectrometer at the Thomas Jefferson National Accelerator Facility, using polarized and unpolarized positron and electron beams at 11 GeV is investigated. This experimental configuration is shown to provide unprecedented access to the GPDs with the determination of the real and imaginary parts of the Compton Form Factor ${\mathcal H}$ in an unexplored phase space, and to enable an exploratory investigation of higher twist effects.

nucl-ex↗

arXiv2021

Synthetic Control over the Binding Configuration of Luminescent sp3-Defects in Single-Walled Carbon Nanotubes

The controlled functionalization of single-walled carbon nanotubes with luminescent sp3-defects has created the potential to employ them as quantum-light sources in the near-infrared. For that, it is crucial to control their spectral diversity. The emission wavelength is determined by the binding configuration of the defects rather than the molecular structure of the attached groups. However, current functionalization methods produce a variety of binding configurations and thus emission wavelengths. We introduce a simple reaction protocol for the creation of only one type of luminescent defect in polymer-sorted (6,5) nanotubes, which is more red-shifted and exhibits longer photoluminescence lifetimes than the commonly obtained binding configurations. We demonstrate single-photon emission at room temperature and expand this functionalization to other polymer-wrapped nanotubes with emission further in the near-infrared. As the selectivity of the reaction with various aniline derivatives depends on the presence of an organic base we propose nucleophilic addition as the reaction mechanism.

physics.app-ph↗

arXiv2021

A hybrid method for overcoming thermal shock of non-contact infrared thermometers

The non-contact infrared thermometer (NCIT) is an important basic tool for fever screening and self-health monitoring. However, it is susceptible to the thermal shock when working in a low temperature environment, which will cause a time-consuming and inaccurate human body temperature measurements. To overcome the effects of thermal shock, a hybrid temperature compensation method combining hardware and algorithm is proposed. Firstly, the principle of infrared temperature measurement is described and the influence of thermal shock on infrared thermometer is analyzed. Then, the hybrid temperature compensation scheme is constructed by mounting a heating ring on the infrared sensor shell, and using the proportional integral derivative (PID) algorithm and the pulse width modulation (PWM) technology to control it heating. In this way, the internal ambient temperature of infrared sensor can be raised rapidly closing to the external ambient temperature, and the stable outputs of the infrared sensor are also accelerated. Finally, some experiments are carried out in a laboratory. The results show that the proposed method can quickly and accurately measure the temperature of standard black body when the ambient temperatures are 5 , 15 and 25 Celsius respectively, the measurement error is only 0.2 Celsius , and the measurement time is less than 2 seconds. This study would be beneficial to improve performance of NCIT, especially the infrared ear thermometer.

physics.med-ph↗

arXiv2021

Beam charge asymmetries for deeply virtual Compton scattering off the proton

The unpolarized and polarized Beam Char\-ge Asymmetries (BCAs) of the $\vv{e}^{\pm}p \to e^{\pm}p γ$ process off unpolarized hydrogen are discussed. The measurement of BCAs with the CLAS12 spectrometer at the Thomas Jefferson National Accelerator Facility, using polarized positron and electron beams at 10.6 GeV is investigated. This experimental configuration allows to measure azimuthal and $t$-dependences of the unpolarized and polarized BCAs over a large $(x_B,Q^2)$ phase space, providing a direct access to the real part of the Compton Form Factor (CFF) ${\mathcal H}$. Additionally, these measurements confront the Bethe-Heitler dominance hypothesis and eventual effects beyond leading twist. The impact of potential positron beam data on the determination of CFFs is also investigated within a local fitting approach of experimental observables. Positron data are shown to strongly reduce correlations between CFFs and consequently improve significantly the determination of $\Re {\rm e} [\mathcal{H}]$.

nucl-ex↗

arXiv2019

Waveform Feature Implementation for FRIB LLRF Controllers

Waveform feature is one of the requirements for the FRIB LLRF controllers. It is desired that the LLRF con-trollers store the internal data (e.g. the amplitude and phase information of forward/reverse/cavity signals) for at least one second of sampled data at the RF feedback control loop rate (around 1.25 MHz). One use case is to freeze the data buffer when an interlock event happens and read out the fast data to diagnose the problem. An-other use case is to monitor a set of signals at a decimated rate (user settable) while the data buffer is still running, like using an oscilloscope. The detailed implementation will be discussed in the paper, including writing data into the DDR memory through the native interface, reading out the data through the bus interface, etc.

physics.ins-det↗

arXiv2018

Single photon emission from graphene quantum dots at room temperature

In the field of condensed matter, graphene plays a central role as an emerging material for nanoelectronics. Nevertheless, graphene is a semimetal, which constitutes a severe limitation for some future applications. Therefore, a lot of efforts are being made to develop semiconductor materials whose structure is compatible with the graphene lattice. In this perspective, little pieces of graphene represent a promising alternative. In particular, their electronic, optical and spin properties can be in principle controlled by designing their size, shape and edges. As an example, graphene nanoribbons with zigzag edges have localized spin polarized states. Likewise, singlet-triplet energy splitting can be chosen by designing the structure of graphene quantum dots. Moreover, bottom-up molecular synthesis put these potentialities at our fingertips. Here, we report on a single emitter study that directly addresses the intrinsic properties of a single graphene quantum dot. In particular, we show that graphene quantum dots emit single photons at room temperature with a high purity, a high brightness and a good photostability. These results pave the way to the development of new quantum systems based on these nanoscale pieces of graphene.

cond-mat.mes-hall↗

arXiv2015

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.

eess.SY↗