Trainable Discrete Feature Embeddings for Quantum Machine Learning
Trainable Discrete Feature Embeddings for Quantum Machine Learning
复制标题
用于量子机器学习的可训练离散特征嵌入
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Raymond H. Putra
中科院分区：
文献类型：
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作者：
Napat Thumwanit;Chayaphol Lortaraprasert;Hiroshi Yano;Raymond H. Putra
量子分类器在希尔伯特空间中提供输入数据的复杂嵌入，有望获得量子优势。这一优势来自于将输入编码为具有可变量子电路的量子态的量子特征映射。最近的一项工作展示了如何使用量子随机访问编码(QRAC)来映射具有较少量子比特的离散特征，QRAC是将二进制串编码为量子态的重要原语。我们提出了一种新的方法，通过结合QRAC和最近提出的一种称为量子度量学习的训练量子特征映射的策略，将离散特征嵌入到可训练量子电路中。我们证明了所提出的可训练嵌入不仅需要与QRAC一样少的量子比特，而且还克服了QRAC在分类基于硬布尔函数的输入分类方面的局限性。我们数值演示了它在变分量子分类器中的使用，以获得更好的分类真实世界数据集的性能，从而它有可能利用近期量子计算机进行量子机器学习1。
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning 1.