An important application for near-term quantum computing lies in optimization
tasks, with applications ranging from quantum chemistry and drug discovery to
machine learning. In many settings --- most prominently in so-called
parametrized or variational algorithms --- the objective function is a result
of hybrid quantum-classical processing. To optimize the objective, it is useful
to have access to exact gradients of quantum circuits with respect to gate
parameters. This paper shows how gradients of expectation values of quantum
measurements can be estimated using the same, or almost the same, architecture
that executes the original circuit. It generalizes previous results for
qubit-based platforms, and proposes recipes for the computation of gradients of
continuous-variable circuits. Interestingly, in many important instances it is
sufficient to run the original quantum circuit twice while shifting a single
gate parameter to obtain the corresponding component of the gradient. More
general cases can be solved by conditioning a single gate on an ancilla.

本文介绍了一种利用同一或几乎相同的架构的方法来估计量子测量期望值的梯度，以优化杂化量子 - 经典算法的目标函数，尤其适用于量子化学、药物发现和机器学习等领域。

在量子硬件上计算解析梯度

Evaluating analytic gradients on quantum hardware

We introduce Strawberry Fields, an open-source quantum programming
architecture for light-based quantum computers, and detail its key features.
Built in Python, Strawberry Fields is a full-stack library for design,
simulation, optimization, and quantum machine learning of continuous-variable
circuits. The platform consists of three main components: (i) an API for
quantum programming based on an easy-to-use language named Blackbird; (ii) a
suite of three virtual quantum computer backends, built in NumPy and
TensorFlow, each targeting specialized uses; and (iii) an engine which can
compile Blackbird programs on various backends, including the three built-in
simulators, and -- in the near future -- photonic quantum information
processors. The library also contains examples of several paradigmatic
algorithms, including teleportation, (Gaussian) boson sampling, instantaneous
quantum polynomial, Hamiltonian simulation, and variational quantum circuit
optimization.

介绍了 Strawberry Fields 开源量子编程体系结构，它使用 Python 语言，适用于设计，模拟，优化和量子计算机的连续变量电路。该平台包括三个主要组成部分：使用 Blackbird 编程的 API，三种虚拟量子计算机后端和一个可以在各种后端上编译 Blackbird 程序的引擎，同时包含几个典型算法的示例。