Recently, machine learning has had remarkable impact in scientific to everyday-life applications. However, complex tasks often require the consumption of unfeasible amounts of energy and computational power. Quantum computation may lower such requirements, although it is unclear whether enhancements are reachable with current technologies. Here we demonstrate a kernel method on a photonic integrated processor to perform a binary classification task. We show that our protocol outperforms state-of-the-art kernel methods such as gaussian and neural tangent kernels by exploiting quantum interference, and provides further improvements in accuracy by offering single-photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result gives access to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

最近，机器学习在科学到日常生活应用中产生了显著影响。但是，复杂的任务通常需要消耗不可行的能源和计算能力。量子计算可能会降低此类要求，尽管目前尚不清楚当前技术是否可以实现增强功能。在这里，我们演示了光子集成处理器上的内核方法，用于执行二元分类任务。我们表明，通过利用量子干扰，我们的协议优于最先进的内核方法，例如高斯和神经切线内核，并通过提供单光子相干性进一步提高了准确性。我们的方案不需要纠缠门，可以通过附加模式和注入光子来修改系统维度。此结果提供了更高效的算法和制定量子效应改进标准方法的任务。