Reinforcement learning algorithms are used in a wide range of applications,
from gaming and robotics to autonomous vehicles. In this paper we describe a
set of reinforcement learning-based local weight update rules and their
implementation in superconducting hardware. Using SPICE circuit simulations, we
implement a small-scale neural network with a learning time of order one
nanosecond. This network can be trained to learn new functions simply by
changing the target output for a given set of inputs, without the need for any
external adjustments to the network. In this implementation the weights are
adjusted based on the current state of the overall network response and locally
stored information about the previous action. This removes the need to program
explicit weight values in these networks, which is one of the primary
challenges that analog hardware implementations of neural networks face. The
adjustment of weights is based on a global reinforcement signal that obviates
the need for circuitry to back-propagate errors.

使用超导硬件实现了一套基于强化学习的局部权重更新规则，并通过 SPICE 电路模拟实现了一个小规模神经网络，学习时间约为一纳秒。这个实现中，权重根据整体网络响应的当前状态和先前动作的本地存储信息进行调整，无需外部调整。