Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.

量子核方法通过有效地学习已编码到指数级大希尔伯特空间的输入数据点之间的关系，显示出加速数据分析的前景。虽然该技术已成功用于合成数据集的小规模实验，但尚未彻底解决在嘈杂硬件上扩展到大型电路的实际挑战。在这里，我们展示了我们使用 Google 的通用量子处理器 Sycamore 对取自宇宙学领域的真实高维数据进行实验实施量子核分类器的发现。我们构建了一个电路 ansatz，它保留了内核幅度，否则这些幅度通常会因希尔伯特空间的指数增长而消失，并实现特定于在近期硬件上计算量子内核的任务的错误缓解。