arXiv subjects

Shabnam Jabeen

Publications and source records attributed to Shabnam Jabeen.

5 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↗

arXiv2025

Quantum parallel information exchange (QPIE) hybrid network with transfer learning

Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns by leveraging quantum systems ability to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network (QNN) frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quantum hardware as unitary rotations introduce overhead from complex number computations, and the quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce quantum parallel information exchange (QPIE) hybrid network, a new non-sequential hybrid classical quantum model architecture, leveraging quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits, which enables efficient pattern recognition and temporal series data prediction by utilizing non-clifford parameterized quantum gates thereby enhancing both learning efficiency and representational capacity. Additionally, we develop a dynamic gradient selection method that applies the parameter shift rule on quantum processing units (QPUs) and adjoint differentiation on GPUs. Our results demonstrate model performance exhibiting higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100-steps, and showcasing a more unbaised eigenvalue spectrum of the fisher information matrix on CPU/GPU and IonQ QPU simulators.

quant-ph↗

arXiv2009

Top quark properties measurement with the $D0$ detector

One of the main goals of the Tevatron RunII is to look for any hints for new physics. At D0, the range of searches for new physics signals is large and one of the places we look for hints for new physics is by measuring the top quark properties. A few of these measurements are discussed in this paper.

hep-ex↗