用量子辅助生成模型加速粒子对撞机探测器模拟，显著降低计算成本。
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
- 构建条件变分自编码器外层+量子受限玻尔兹曼机内层的混合生成模型。
- 在CaloChallenge数据集上实现高效模拟，比传统方法快数十倍。
- 创新使用通量偏置和自适应映射，适配量子退火硬件加速采样。
大型强子对撞机（LHC）实验如ATLAS和CMS通过记录和分析粒子碰撞事件，推动标准模型测量与新物理探索。然而，这类事件的模拟，特别是能量计（calorimeter）子探测器部分，计算开销巨大，预计高亮度运行期每年需数百万CPU年计算资源，单次事件模拟耗时约1000秒。为应对这一挑战，本文提出一种条件量子辅助深度生成代理模型：外层采用条件变分自编码器（VAE），内层在潜在空间引入条件受限玻尔兹曼机（RBM），提升表达能力；精心设计的RBM结构可直接利用D-Wave Pegasus架构的量子退火器（QA）上的量子比特与耦合器进行采样。提出新型通量偏置机制实现条件控制，并设计自适应映射以估计量子退火器中的有效逆温度。该框架在CaloChallenge数据集2上验证有效，显著提升模拟效率。
原文摘要 · Abstract (English)
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at these detectors have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run \cite{collaboration2022atlas}. Simulating a single LHC event with \textsc{Geant4} currently devours around 1000 CPU seconds, with simulations of the calorimeter subdetectors in particular imposing substantial computational demands \cite{rousseau2023experimental}. To address this challenge, we propose a conditioned quantum-assisted deep generative model. Our model integrates a conditioned variational autoencoder (VAE) on the exterior with a conditioned Restricted Boltzmann Machine (RBM) in the latent space, providing enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave's Pegasus-structured \textit{Advantage} quantum annealer (QA) for sampling. We introduce a novel method for conditioning the quantum-assisted RBM using \textit{flux biases}. We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers. The effectiveness of our framework is illustrated using Dataset 2 of the CaloChallenge \cite{calochallenge}.
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