经典和光子量子计算机上的无监督事件分类
Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
摘要：
光子量子计算机提供了优于基于离散量子位的量子计算范例的若干益处。通过利用连续变量计算的能力，我们构建了一个异常检测模型，用于搜索新物理学。我们的模型使用高斯玻色子采样，这是一个$\#$ P-hard问题，因此无法有效地访问经典设备。这用于从图形数据创建特征向量，图形数据是表示高能碰撞事件数据的自然格式。一个简单的K-均值聚类算法是用来提供一个基线的分类方法。然后，我们提出了一种新的异常检测方法，结合使用高斯玻色子采样和量子扩展K-均值称为Q-均值。这被发现给等效的结果相比，经典的聚类版本，同时也降低了$\mathcal{O}$的复杂性，相对于样本的特征向量长度，从$\mathcal{O}(N)$到$\mathcal{O}(\mbox{log}(N))$。由于采样算法的速度和近期光子量子器件的可行性，触发级的异常检测在未来的LHC运行中可能变得实用。
英文摘要：
Photonic Quantum Computers provides several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a $\#$P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the $\mathcal{O}$ complexity, with respect to the sample's feature-vector length, from $\mathcal{O}(N)$ to $\mathcal{O}(\mbox{log}(N))$. Due to the speed of the sampling algorithm and the feasibility of near-term photonic quantum devices, anomaly detection at the trigger level can become practical in future LHC runs.
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