arXiv subjects

Jinghong Yang

Publications and source records attributed to Jinghong Yang.

8 recordsLinked to original sources

arXiv2026

Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware

Jets are central to the physics programs of both current and future colliders, from precision Standard Model measurements and searches for new physics at the Large Hadron Collider to studies of nucleon structure at the future Electron-Ion Collider. Motivated by these applications, we explore quantum machine learning for jet classification and present a permutation-invariant Quantum Graph Neural Network (QGNN) applied to particle-cloud representations of jets. We apply the model to two such discrimination tasks: quark vs. gluon and up vs. down quark flavor tagging, with the latter being, to our knowledge, the first application of a quantum model to this problem. In the ideal simulation, the QGNN performs competitively against the Particle Flow Network and traditional QCD observables. We further deploy scaled-down models to IBM and IonQ quantum processing units (QPUs), where we train and evaluate them, obtaining promising results. Finally, we perform an interpretability analysis to characterize the observables learned by the quantum model, relating them to generalized angularities for the quark-gluon study and to jet charge for the flavor study.

hep-ph↗

arXiv2026

Tightening energy-based boson truncation bound using Monte Carlo-assisted methods

Quantum simulation offers a promising framework for quantum field theory calculations. Obtaining reliable results, however, requires careful characterization of systematic uncertainties. One important source is the boson truncation error, which arises from representing infinite-dimensional local Hilbert spaces with finite-dimensional ones. Previous studies have examined this problem from several perspectives. In particular, Jordan, Lee, and Preskill (arXiv:1111.3633) derived an energy-based bound applicable to generic low-energy states across a broad class of field theories. However, this approach often yields overly conservative bounds, especially at large volumes. In this work, we introduce a new methodology that significantly tightens the energy-based boson truncation bound through two complementary advances: an improved analytic derivation and a Monte Carlo-based numerical procedure. We demonstrate the method in (1+1)-dimensional scalar field theory and (2+1)-dimensional U(1) gauge theory in the dual formalism. Our approach substantially mitigates the volume dependence of the required truncation cutoff, achieving reductions nearly proportional to the volume in some cases and to the square root of the volume in others.

hep-lat↗

arXiv2025

Quantum Machine Learning for State Tomography Using Classical Data

Reconstructing quantum states from measurement data represents a formidable challenge in quantum information science, especially as system sizes grow beyond the reach of traditional tomography methods. While recent studies have explored quantum machine learning (QML) for quantum state tomography (QST), nearly all rely on idealized assumptions, such as direct access to the unknown quantum state as quantum data input, which are incompatible with current hardware constraints. In this work, we present a QML-based tomography protocol that operates entirely on classical measurement data and is fully executable on noisy intermediate-scale quantum (NISQ) devices. Our approach employs a variational quantum circuit trained to reconstruct quantum states based solely on measurement outcomes. We test the method in simulation, achieving high-fidelity reconstructions of diverse quantum states, including GHZ states, spin chain ground states, and states generated by random circuits. The protocol is then validated on quantum hardware from IBM and IonQ. Additionally, we demonstrate accurate tomography is possible using incomplete measurement bases, a crucial step towards scaling up our protocol. Our results in various scenarios illustrate successful state reconstruction with fidelity reaching 90% or higher. To our knowledge, this is the first QML-based tomography scheme that has been implemented on real quantum processors using exclusively classical measurements. This work establishes the feasibility of QML-based tomography on current quantum platforms and offers a scalable pathway for practical quantum state reconstruction.

quant-ph↗

arXiv2024

Small-$x$ gluon GPD constrained from deeply virtual $J/ψ$ production and gluon PDF through universal-moment parameterization

We phenomenologically constrain the small-$x$ and small-$ξ$ gluon generalized parton distributions (GPDs) with the deeply virtual $J/ψ$ production (DV$J/ψ$P) in the framework of GPDs through universal moment parameterization (GUMP). We use a hybrid cross-section formula combining collinear factorization to the next-to-leading order (NLO) accuracy of the strong coupling $α_s$, with corrections from non-relativistic QCD to account for the power corrections due to the heavy $J/ψ$ mass. We reach reasonable fit to the measured differential cross-sections of DV$J/ψ$P by H1 at Hadron-Electron Ring Accelerator (HERA) as well as forward gluon PDFs from JAM22 global analysis. We find that both NLO and non-relativistic corrections are significant for heavy vector meson productions. Of course, the gluon GPD we obtain still contain considerable freedom in need of inputs from other constraints, particularly in the distribution-amplitude-like region.

hep-ph↗

arXiv2023

Updated analysis of near-threshold heavy quarkonium production for probe of proton's gluonic gravitational form factors

There has been growing interest in the near-threshold production of heavy quarkonium which can access the gluonic structure in the nucleon. Previously we studied this process with quantum chromodynamics (QCD) and showed that it can be factorized with the gluon generalized parton distributions (GPDs) in the heavy quark limit. We further argued that the hadronic matrix element is dominated by its leading moments corresponding to the gluonic gravitational form factors (GFFs) in this limit. Since then, there have been many new developments on this subject. More experimental measurements have been made and published, and the lattice simulation of gluonic GFFs has been improved as well. In this work, we make an important revision to a previous result and perform an updated analysis with the new inputs. We also study the importance of the large momentum transfer to extract these gluonic structures reliably in this framework.

hep-ph↗

arXiv2023

Understanding Jet Charge

The jet charge is an old observable that has proven uniquely useful for discrimination of jets initiated by different flavors of light quarks, for example. In this Letter, we propose an approach to understanding the jet charge by establishing simple, robust assumptions that hold to good approximation non-perturbatively, such as isospin conservation and large particle multiplicity in the jets, forgoing any attempt at a perturbative analysis. From these assumptions, the jet charge distribution with fixed particle multiplicity takes the form of a Gaussian by the central limit theorem and whose mean and variance are related to fractional-power moments of single particle energy distributions. These results make several concrete predictions for the scaling of the jet charge with the multiplicity, explaining many of the results already in the literature, and new results we validate in Monte Carlo simulation.

hep-ph↗

arXiv2023

Generalized parton distributions through universal moment parameterization: non-zero skewness case

We present the first global analysis of generalized parton distributions (GPDs) combing lattice quantum chromodynamics (QCD) calculations and experiment measurements including global parton distribution functions (PDFs), form factors (FFs) and deeply virtual Compton scattering (DVCS) measurements. Following the previous work where we parameterize GPDs in terms of their moments, we extend the framework to allow for the global analysis at non-zero skewness. Together with the constraints at zero skewness, we fit GPDs to global DVCS measurements from both the recent JLab and the earlier Hadron-Electron Ring Accelerator (HERA) experiments with two active quark flavors and leading order QCD evolution. With certain choices of empirical constraints, both sea and valence quark distributions are extracted with the combined inputs, and we present the quark distributions in the proton correspondingly. We also discuss how to extend the framework to accommodate more off-forward constraints beyond the small $ξ$ expansion, especially the lattice calculated GPDs.

hep-ph↗

arXiv2021

A Framework for Simultaneously Measuring Field Densities and the High-z Luminosity Function

Cosmic variance from large-scale structure will be a major source of uncertainty for galaxy surveys at z > 6, but that same structure will also provide an opportunity to identify and study dense environments in the early Universe. Using a robust model for galaxy clustering, we directly incorporate large-scale densities into an inference framework that simultaneously measures the high-z (z > 6) UV luminosity function and the average matter density of each distinct volume in a survey. Through this framework, we forecast the performance of several major upcoming James Webb Space Telescope (JWST) galaxy surveys. We find that they can constrain field matter densities down to the theoretical limit imposed by Poisson noise and unambiguously identify over-dense (and under-dense) regions on transverse scales of tens of comoving Mpc. We also predict JWST will measure the luminosity function with a precision at z = 12 comparable to existing Hubble Space Telescope's constraints at z = 8 (and even better for the faint-end slope). We also find that wide-field surveys are especially important in distinguishing luminosity function models.

astro-ph.CO↗