Quantum Computing and Machine Learning Workshop 2024

Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known as jets. Accurate jet clustering is crucial as it retains the information of the originating quark or gluon and forms the basis for studying...

Particle identification (PID) is crucial in particle physics experiments. A promising breakthrough in PID involves cluster counting, which quantifies primary ionizations along a particle’s trajectory in a drift chamber (DC), rather than relying on traditional dE/dx measurements. However, a significant challenge in cluster counting lies in developing an efficient reconstruction algorithm to...

Measuring neutrino mass ordering (NMO) poses a fundamental challenge in neutrino physics. To address this, the Jiangmen Underground Neutrino Observatory (JUNO) experiment is scheduled to commence data collection in late 2024, aiming to determine the NMO at a 3-sigma confidence level within 6 years. A key factor in achieving this is ensuring a high-quality energy resolution of positrons....

Detecting Gamma-Ray Burst (GRB) signals from triggerless data poses significant challenges due to high noise levels, a problem similarly encountered in the Large High Altitude Air Shower Observatory's Water Cherenkov Detector Array (LHAASO-WCDA) triggerless data analysis. This research aims to enhance the GRB triggerless data algorithm that leverages gamma-ray showers' distinct spatial...

Accurate track reconstruction is essential for high sensitivity to beyond Standard Model (BSM) signatures. However, many BSM particles undergo interactions that produce non-helical trajectories, which are difficult to incorporate into traditional tracking techniques.

We present a version of the ML-based GNN4ITk track reconstruction pipeline, applied to a custom detector environment for...

BESIII provides an important research platform for physics topics in the tau-charm energy region. The physics processes in the tau-charm energy region involve a significant amount of hadronic components in the final state, making particle identification crucial.
Currently, BESIII achieves particle identification through the combination of dE/dx and TOF measurements. Due to the absence of a...

This presentation delves into integrating quantum computing into transformer architectures to enhance High Energy Physics (HEP) analysis performance. By encoding classical HEP data into quantum states using a quantum-trainable circuit, we aim to harness the strengths of both quantum and classical computing. This hybrid approach is designed to improve data processing and analysis. The...

We propose a new approach to learning powerful jet representations directly from unlabelled data. The method employs a Particle Transformer to predict masked particle representations in a latent space, overcoming the need for discrete tokenization and enabling it to extend to arbitrary input features beyond the Lorentz four-vectors. We demonstrate the effectiveness and flexibility of this...

In this presentation, we discuss the latest developments and applications of Multivariate Analysis (MVA) techniques within the Belle II experiment. The Belle II experiment, operating at the SuperKEKB accelerator in Japan, aims to explore the fundamental interactions of particles and test the limits of the Standard Model of particle physics.

MVA techniques are essential tools in the analysis...

High-energy physics relies on large and accurate samples of simulated events, but generating these samples with GEANT4 is CPU intensive. The ATLAS experiment has employed generative adversarial networks (GANs) for fast shower simulation, which is an important approach to solving the problem. Quantum GANs, leveraging the advantages of quantum computing, have the potential to outperform standard...

We apply machine-learning techniques to the effective-field-theory analysis of the $e^+e^- \to W^+W^-$ processes at future lepton colliders, and demonstrate their advantages in comparison with conventional methods, such as optimal observables. In particular, we show that machine-learning methods are more robust to %effects of systematic uncertainties, initial state radiations
detector...

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. In this talk, we introduce a novel experimental method,...

Exact solutions to combinatorial optimization problems are challenging to obtain using classical computing. The current tenet in the field is that quantum computers can address these problems more efficiently. While promising algorithms require fault-tolerant quantum hardware, variational algorithms have emerged as viable candidates for near-term devices. The success of these algorithms hinges...

量子技术的发展带来了量子比特数目和质量的提升，然而现有硬件上所能支持的量子算法的复杂度仍然有限。发展近期应用仍然是短期量子算法发展的重要议题。量子变分算法是近期应用中重要的算法框架，其设计依赖于线路拟设的选择和参数的优化方案。数字化反向透热补偿算法是一种高效的量子变分算法。其算法的设计源自对量子控制中的反向透热补偿算法的Trotter处理。从动力学层面加速了演化的过程，缩短了线路的长度。同时反向透热补偿算法也对线路的参数提供了最优解方案，可以获得较好的算法性能。如果在此基础上对参数做进一步优化，可以更快的收敛速度，并降低算法优化到局部极小值的概率。本报告将结合分解问题和自旋问题的最优解问题，讨论数字化反向透热补偿算法的优势和局限。 算法作为相比于QAOA更有效的算法，可以解决常见组合优化问题。

我国同步辐射实验装置硬设施建设的水平、数量均跻身世界前列，但分析破解海量、多模态实验数据的软设施建设却发展严重滞后，直接阻碍了重大科学突破的发现与产出。报告人基于机器学习方法驱动，结合多尺度模拟、数字孪生等技术，构建了先进同步辐射实验“智慧终端”多维解析计算系统，实现多种重要材料的结构及性能的精准解析，推动了包括新型纳米药物、功能新材料性能提升的应用研究。
相关代表论文：
1. Molybdenum derived from nanomaterials incorporates into molybdenum enzymes and affects their activities in vivo, Nature Nanotechnology, 16, 708 (2021)
2. Water-Regulated Lead Halide Perovskites...