The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work sheds some light on the unique advantages offered by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks.

中文翻译：

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量子机器学习模型的 Fock 状态增强表达能力

数据嵌入过程是量子机器学习的瓶颈之一，可能会否定任何量子加速。鉴于此，需要更有效的数据编码策略。我们提出了一种基于光子的玻色子数据编码方案，该方案使用较少的编码层嵌入经典数据点，并通过将数据点映射到高维 Fock 空间来规避对非线性光学组件的需求。电路的表达能力可以通过输入光子的数量来控制。我们的工作揭示了量子光子学在量子机器学习模型的表达能力方面所提供的独特优势。通过利用依赖于光子数的表达能力，