Self-training superconducting neuromorphic circuits using reinforcement learning rules
This work addresses a key bottleneck for analog neuromorphic hardware by enabling self-training without external adjustments, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackled the challenge of programming explicit weight values in analog hardware neural networks by developing reinforcement learning-based local weight update rules implemented in superconducting circuits, achieving a learning time of about one nanosecond in SPICE simulations.
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.