Variational quantum algorithms (VQAs) combining the advantages of
parameterized quantum circuits and classical optimizers, promise practical
quantum applications in the Noisy Intermediate-Scale Quantum era. The
performance of VQAs heavily depends on the optimization method. Compared with
gradient-free and ordinary gradient descent methods, the quantum natural
gradient (QNG), which mirrors the geometric structure of the parameter space,
can achieve faster convergence and avoid local minima more easily, thereby
reducing the cost of circuit executions. We utilized a fully programmable
photonic chip to experimentally estimate the QNG in photonics for the first
time. We obtained the dissociation curve of the He-H$^+$ cation and achieved
chemical accuracy, verifying the outperformance of QNG optimization on a
photonic device. Our work opens up a vista of utilizing QNG in photonics to
implement practical near-term quantum applications.

利用全程可编程的光子芯片首次在光子学中实验估计量子自然梯度（QNG），获得了 He-H$^+$ 阳离子的解离曲线并实现了化学精度，验证了 QNG 优化在光子学设备上的超越性，为利用 QNG 在光子学中实现实用的短期量子应用打开了新的视野。