Quantum Computing and Machine Learning Workshop 2025

Next generation high-energy experiments aim to probe unexplored regimes in particle physics. Real-time data processing of terabyte-per-second is a critical technological bottleneck, especially, track reconstruction suffer from degraded efficiency under high-background environment. To address this challenge, we are designing a heterogeneous data processing system that integrates a real-time...

量子计算在凝聚态物理、高能物理、量子化学等诸多领域的计算任务中展现出巨大的潜力。为了应用量子计算技术，研究人员和开发者需要编写量子程序。编程过程可能出现错误，然而量子计算独特的性质使得发现和定位量子程序中的错误存在困难。例如，由于测量导致塌缩使得无法跟踪程序运行中间变量的值；错误对运行结果的影响难以观察等。本报告将关注“正确地编写量子程序”这一越来越重要的主题。报告将首先分享几类典型的量子程序的编程错误，并给出对应的发现和避免的方案；然后简要介绍量子软件工程这一新兴的研究领域；最后介绍高能所计算中心近年来在量子程序开发工具方面的工作和计划。

Quantum Machine Learning (QML) is an advanced data analysis technique, which can detect data structures within massive datasets, building models to achieve data prediction, classification, or simulation, with less human intervention. However, the practical viability of QML still remains a topic of debate, requiring more examples of real data analysis with quantum hardware for its further...

In recent years, the quantum computing method has been used to address the sign problem in traditional Monte Carlo lattice gauge theory (LGT) simulations. We propose that the Coulomb gauge (CG) should be used in quantum simulations of LGT. Since the redundant degrees of freedom of gauge fields can be eliminated in CG, the Hamiltonian in CG does not need to be gauge invariance, allowing the...

The $n$-time correlation function is pivotal for establishing connections between theoretical predictions and experimental observations of a quantum system. Conventional methods for computing $n$-time correlation functions on quantum computers, such as the Hadamard test, generally require an ancilla qubit that controls the entire system -- an approach that poses challenges for digital quantum...

We implement a variational quantum algorithm to investigate the chiral condensate in a 1+1 dimensional SU(2) non-Abelian gauge theory. The algorithm is evaluated using a proposed Monte Carlo sampling method, which allows the extension to large qubit systems. The obtained results through quantum simulations on classical and actual quantum hardware are in good agreement with exact...

The Large High Altitude Air Shower Observatory (LHAASO) stands at the forefront of high-energy astronomy, with core physics goals including the identification of galactic PeVatrons, indirect dark matter searches, and cosmic ray origin studies. However, achieving these objectives is challenged by the critical need to process data streams while maintaining real-time background rejection and...

In this paper, we establish a theoretical connection between complex-valued neural networks (CVNNs) and fermionic quantum field theory (QFT), bridging a fundamental gap in the emerging framework of neural network quantum field theory (NN-QFT). While prior NN-QFT works have linked real-valued architectures to bosonic fields, we demonstrate that CVNNs equipped with tensor-valued weights...

Quantum entanglement is a hallmark feature of quantum mechanics, manifesting as correlations between subsystems that cannot be fully described without one another, regardless of spatial separation. While entanglement has been observed in processes such as $pp\to t \bar{t}$ and thoroughly analyzed in Higgs decay channels ($H\to VV$) at the Large Hadron Collider (LHC), it remains comparatively...

Long-lived neutral hadrons, including (anti-)neutron and $K^0_L$ meson, are important probes for physics in the tau-charm energy region. However, most tau-charm facilities do not include dedicated hadronic calorimeters, and their neutral hadron detection must rely on the electromagnetic calorimeter (EMC). Because the EMC's small volume and dense material only partially contain hadronic...

Particle flow reconstruction has become the standard paradigm for event reconstruction at current high-energy collider experiments. Its ultimate goal is to establish a one-to-one (1-1) correspondence between reconstructed and truth incident particles, while achieving highly efficient particle identification. In realistic experimental environments, however, this ideal correspondence is...

The Super Tau Charm Facility (STCF) is a next-generation electron-positron collider proposed in China, operating at a center-of-mass energy range of 2–7 GeV with a peak luminosity of 0.5×10³⁵ cm⁻²s⁻¹. In high-energy experiments, the identification of high-momentum charged hadrons is crucial for studying various physics processes. At STCF, the particle identification (PID) system based on...

The High Energy cosmic-Radiation Detection (HERD) facility will be installed as a space astronomy payload on the China Space Station in 2028. The three-dimensional imaging calorimeter (CALO) of HERD comprises about 7500 lutetium yttrium oxy-orthosilicate (LYSO:Ce) cubes, where the topological development of hadronic showers can be measured. Over the years, advancements in deep learning,...

The High Energy Cosmic-Radiation Detection Facility (HERD) is dedicated to achieving several scientific objectives, including the search for dark matter, precise measurement of the cosmic ray spectrum, and gamma-ray sky survey observations.
HERD’s innovative design incorporates a three-dimensional imaging calorimeter with five sensitive faces, significantly enhancing geometric acceptance....

The Electromagnetic Calorimeter (ECAL) in the AMS-02 experiment is a 3D imaging detector and plays a pivotal role in various physics analysis results. The precise reconstruction of electromagnetic shower axis in the ECAL contributes to a better understanding of its performance in particle identification as well as pointing capability of gamma rays. Conventional methods reconstruct the shower...

eXTP卫星是我国下一代旗舰级X射线天文台，其中的PFA载荷对能量为2-10 keV的X射线进行可成像的偏振、时变和能谱观测。PFA载荷的气体X射线偏振探测器基于X射线在气体探测器中发生光电效应时光电子出射方向在垂直于X射线入射方向的平面内的角分布来测量偏振。在这类探测器的数据分析中，最关键的就是光电子径迹的重建。由于光电子径迹是通过测量光电子在气体探测器内与气体分子碰撞而产生的电离电子所描绘的径迹而获得的。光电子在运动过程中，除了电离出电子，还会因为和气体分子中的原子核发生库里散射而改变方向，同时还会伴随有俄歇电子的产生，所有这些因素增加了光电子径迹重建的复杂性。传统的光电子径迹重建方法是力矩法，随着机器学习技术的发展，我们开展了基于深度学习的地面和在轨光电子径迹重建方法的研究，本报告将介绍相关研究情况。

A foundation jet model aims to achieve optimal performance across all jet analysis tasks while ensuring strong generalization. Building onSophon, a pre-trained jet classification model, we developSophon++, which employs contrastive learning to connect initial, parton-level, and reconstruction-level particles, enabling continuous encoding of generator-level particle configurations into...

In the large liquid scintillator detector JUNO, $\beta$-decays of $^{14}\mathrm{C}$ inevitably deposit energy within the detector, producing scintillation light. These photons can potentially overlap with positron ($e^{+}$) signals, forming pile-up events. This pile-up effect can consequently impact the fine reconstruction of the $e^{+}$ signal. We have employed three distinct machine learning...

As a cornerstone scientific payload aboard NASA's Fermi Gamma-ray Space Telescope, the Gamma-ray Burst Monitor (GBM) is specifically designed to detect and study cosmic gamma-ray bursts (GRBs) and other high-energy transients, such as solar flares, magnetar outbursts, and Terrestrial Gamma-ray Flashes (TGFs). The primary observational challenge for GBM stems from the inherent complexity and...

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory...

In recent years, we have proposed several novel machine learning algorithms and applied them to the field of accelerator physics (including lattice design, high-frequency cavity optimization, linear accelerator design, etc.), achieving promising results and publishing a series of SCI papers. We also pioneered the application of quantum machine learning in accelerator physics, demonstrating its...