论文标题
有条件出生的机器蒙特卡洛活动一代
Conditional Born machine for Monte Carlo event generation
论文作者
论文摘要
生成建模是近期量子设备的有前途的任务，它可以将量子测量的随机性作为随机来源。所谓的出生机器是纯粹的量子模型，并且有望以量子的方式生成概率分布，而对古典计算机无法访问。本文介绍了出生的机器在蒙特卡洛模拟中的应用，并将其覆盖范围扩展到多元和有条件的分布。模型在（嘈杂）模拟器和IBM量子超导量子硬件上运行。 更具体地说，出生的机器用于生成由Muons和探测器材料之间的散射过程和高能量物理煤层实验中的探测器材料产生的事件。 MFC是出现在超出标准模型理论框架中的玻色子，它们是暗物质的候选者。经验证据表明，出生的机器可以从蒙特卡洛模拟中重现数据集的边际分布和相关性。
Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as a random source. So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers. This paper presents an application of Born machines to Monte Carlo simulations and extends their reach to multivariate and conditional distributions. Models are run on (noisy) simulators and IBM Quantum superconducting quantum hardware. More specifically, Born machines are used to generate muonic force carrier (MFC) events resulting from scattering processes between muons and the detector material in high-energy physics colliders experiments. MFCs are bosons appearing in beyond-the-standard-model theoretical frameworks, which are candidates for dark matter. Empirical evidence suggests that Born machines can reproduce the marginal distributions and correlations of data sets from Monte Carlo simulations.
