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量子机器学习中隐藏的瓶颈：将数据输入量子计算机
量子机器学习有望访问指数级大的表示空间，但在进行任何计算之前，必须首先将经典数据嵌入到量子系统中。本文探讨了 QML 中最容易被忽视的瓶颈之一：有效地将数据输入量子计算机。《量子机器学习中隐藏的瓶颈：将数据输入量子计算机》一文首先出现在《走向数据科学》上。
来源:走向数据科学现代人工智能 (AI) 和机器学习 (ML) 严重依赖于处理大量数据并从中学习模式。 In general, a model’s ability to generalise improves as the amount of available data increases.然而，当我们从经典机器学习转向量子机器学习（QML）时，我们遇到的首要挑战之一是量子计算机无法直接读取经典比特。 Before any computation can happen, the data must first be embedded into quantum states (qubits).
This may sound simple at first, but in practice it is surprisingly difficult. As the size and complexity of the data increase, the cost of preparing these quantum states can grow exponentially. In fact, no universally efficient method for loading arbitrary classical data into quantum systems is currently known.
在本文中，我们将探讨为什么存在这个问题，研究一些常见的量子数据嵌入技术，最后讨论研究人员正在研究的一些现代方法来克服这些限制。
Neural Networks (NNs) are one of the foundational building blocks of modern Machine Learning. Much of their success comes from our growing ability to collect, store, and process massive amounts of data.
At their core, neural networks are mathematical systems designed to learn patterns from data. During training, they gradually adjust their internal parameters to capture the relationships that generated the data in the first place. This allows them to perform tasks such as prediction, generation, and classification.
例如：
qubits as: