论文标题
评估参数化量子电路：关于分类精度，表现能力和纠缠能力之间的关系
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
论文作者
论文摘要
在寻找量子优势时的一个积极调查领域是量子机学习。尤其是混合量子古典设置中的量子机学习和参数化的量子电路，可以通过利用希尔伯特空间的高维度作为特征空间来带来准确性的进步。但是，量子电路均匀地解决希尔伯特空间的能力是否是分类准确性的良好指标？在我们的工作中，我们使用先前ART的方法和量化来进行数值研究，以评估相关水平。我们发现电路均匀地解决希尔伯特空间的能力与需要单个嵌入层的电路的分类精度之间存在很强的相关性，然后是1或2个电路设计。这是基于我们的研究，其中包括1和2层配置中的19个电路，在9个数据集中评估了增加难度。未来的工作将评估是否适用于不同的电路设计。
An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1 and 2 layer configuration, evaluated on 9 datasets of increasing difficulty. Future work will evaluate if this holds for different circuit designs.
