Integrated optics is an engineering solution proposed for exquisite control
of photonic quantum information. Here we use silicon photonics and the linear
combination of quantum operators scheme to realise a fully programmable
two-qubit quantum processor. The device is fabricated with readily available
CMOS based processing and comprises four nonlinear photon-sources, four
filters, eighty-two beam splitters and fifty-eight individually addressable
phase shifters. To demonstrate performance, we programmed the device to
implement ninety-eight various two-qubit unitary operations (with average
quantum process fidelity of 93.2$\pm$4.5%), a two-qubit quantum approximate
optimization algorithm and efficient simulation of Szegedy directed quantum
walks. This fosters further use of the linear combination architecture with
silicon photonics for future photonic quantum processors.

本文采用硅光子学和量子算符线性组合技术实现了一个全编程的双量子比特量子处理器，并在其中编程实现了 98 种不同的二量子比特幺正运算以及有效模拟了 Szegedy 定向量子行走，为未来的光子量子处理器的使用打下了基础。

实现任意两比特处理的大规模硅量子光学

Large-scale silicon quantum photonics implementing arbitrary two-qubit  processing

Recently, integrated optics has gained interest as a hardware platform for
implementing machine learning algorithms. Of particular interest are artificial
neural networks, since matrix-vector multi- plications, which are used heavily
in artificial neural networks, can be done efficiently in photonic circuits.
The training of an artificial neural network is a crucial step in its
application. However, currently on the integrated photonics platform there is
no efficient protocol for the training of these networks. In this work, we
introduce a method that enables highly efficient, in situ training of a
photonic neural network. We use adjoint variable methods to derive the photonic
analogue of the backpropagation algorithm, which is the standard method for
computing gradients of conventional neural networks. We further show how these
gradients may be obtained exactly by performing intensity measurements within
the device. As an application, we demonstrate the training of a numerically
simulated photonic artificial neural network. Beyond the training of photonic
machine learning implementations, our method may also be of broad interest to
experimental sensitivity analysis of photonic systems and the optimization of
reconfigurable optics platforms.

本文提出了一种新的方法，使用伴随变量方法导出光子反向传播算法的光学模拟，并通过在设备内进行强度测量来准确获得这些梯度，从而大大提高了光学神经网络的训练效率。