TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance
TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance
复制标题
TensorFlow Quantum：量子状态准备对量子机器学习性能的影响
作者：
Daniel Sierra;Michael Telahun;Adel Said Elmaghraby
量子器件上的学习方法已经显示出利用量子特性的优势。在机器学习技术中使用量子计算的一个要求是将数据表示为量子态。在量子机器学习中，量子态制备对于在模型中获得功能管道至关重要。一种状态准备方法，振幅编码，允许数据集被更鲁棒地映射或编码，并增强量子模型的学习。虽然更密集地表示，但通过幅度编码准备的数据集为模型提供了更可学习的输入。使用幅度编码的两个主要优点是提高分类精度和减少学习历元之间的可变性。在本文中，我们比较了TensorFlow Quantum的量子卷积神经网络和使用角度编码的混合量子经典网络的基本实现，以及我们设计的第三个网络，该网络利用幅度编码进行富集状态准备。我们的研究结果表明，在训练TensorFlow量子混合量子经典模型之前进行幅度编码有直接的好处。在最好的情况下，幅度编码使分类样本的准确率提高了8.9%。
Learning methodologies on quantum devices have shown that there are advantages in utilizing quantum properties. A requirement for using quantum computing in machine learning techniques is the data representation as quantum states. In Quantum Machine Learning, quantum state preparation is paramount to attain a functional pipeline in a model. One state preparation method, amplitude encoding, allows a dataset to be mapped or encoded more robustly and enhances the learning of quantum models. Albeit more densely represented, a dataset which has been prepared by amplitude encoding provides a more learnable input to a model. The two main advantages from using amplitude encoding are an increase in classification accuracy and reduced variability of learning epoch to epoch. In this paper, we compare the basic implementations of TensorFlow Quantum’s Quantum Convolutional Neural Network and a hybrid quantum-classical network using angle encoding, with a third network of our design that utilizes amplitude encoding for enriched state preparation. Our results show there is a direct benefit in performing amplitude encoding before training a TensorFlow Quantum hybrid quantum-classical model. In the best case scenario, amplitude encoding made classifying the samples 8.9% more accurate.