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