Quantum Computing and Machine Learning Workshop 2025

Contribution List

6. Computing n-time correlation functions without ancilla qubits

Xiaoyang Wang (P)

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...

4. Machine Learning for Parton-Level Studies of Quantum Entanglement Using pp→ττ

Baihong Zhou (Tsung-Dao Lee Institute, Shanghai Jiao Tong University)

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...

5. Quantum simulations of quantum electrodynamics in Coulomb gauge

Li Tianyin (South China Normal University)

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...

8. Studying Hadronic Shower Development in HERD CALO with Machine Learning

Xiao-Fan Tang (IHEP)

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,...

7. Transfer learning empowers material Z classification with muon tomography

Mr 浩辰 王 (合肥工业大学)

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...

2. 基于机器学习的eXTP卫星PFA载荷地面和在轨数光电子径迹重建

Dr 维春 姜 (高能所)

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。