Hybrid quantum classical graph neural networks for particle track reconstruction
Hybrid quantum classical graph neural networks for particle track reconstruction
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DOI：
10.1007/s42484-021-00055-9
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发表时间：
2021-09
影响因子：
4.8
通讯作者：
Cenk Tüysüz;C. Rieger;Kristiane Novotny;B. Demirköz;D. Dobos;K. Potamianos;S. Vallecorsa;J. Vlimant;Richard Forster
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作者：
Cenk Tüysüz;C. Rieger;Kristiane Novotny;B. Demirköz;D. Dobos;K. Potamianos;S. Vallecorsa;J. Vlimant;Richard Forster
欧洲核子研究中心（CERN）的大型强子对撞机（LHC）将进行升级，进一步提高粒子碰撞的瞬时速率（光度），成为高光度LHC （HL-LHC）。光度的增加将显著增加与探测器相互作用的粒子数量。粒子与探测器的相互作用被称为“撞击”。HL-LHC将产生更多的探测器撞击，这将通过使用重建算法来确定这些撞击的粒子轨迹，从而提出一个组合挑战。这项工作探索了将一种新的图神经网络模型转换为一种混合量子-经典图神经网络的可能性，该模型可以最佳地考虑到跟踪探测器数据的稀疏性质及其复杂的几何形状，这种混合量子-经典图神经网络受益于使用变分量子层。我们证明了这种混合模型可以执行类似的经典方法。此外，我们还探讨了具有不同可表达性和纠缠能力的参数化量子电路（PQC），并比较了它们的训练性能，以量化预期收益。这些结果可用于构建未来的路线图，以进一步发展基于电路的混合量子-经典图神经网络。
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.