论文标题
量子电路的无优化分类和密度估计
Optimisation-free Classification and Density Estimation with Quantum Circuits
论文作者
论文摘要
我们证明了一种新型的机器学习框架，以使用量子电路进行概率密度估计和分类。该框架通过量子特征图将训练数据集或单个数据样本映射到物理系统的量子状态。任意大型训练数据集的量子状态总结了其在有限维量子波函数中的概率分布。通过将新数据样本的量子状态投影到训练数据集的量子状态上，可以得出统计信息以对新数据样本的密度进行分类或估计。值得注意的是，我们在实际量子设备上的框架实现不需要对量子电路参数进行任何优化。尽管如此，我们讨论了一种差异量子电路方法，该方法可以利用我们的框架量子优势。
We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. The quantum state of the arbitrarily large training data set summarises its probability distribution in a finite-dimensional quantum wave function. By projecting the quantum state of a new data sample onto the quantum state of the training data set, one can derive statistics to classify or estimate the density of the new data sample. Remarkably, the implementation of our framework on a real quantum device does not require any optimisation of quantum circuit parameters. Nonetheless, we discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.
