Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor

TL;DR

This paper demonstrates a method to implement quantum neural networks using repeat-until-success circuits with real-time feedback on a superconducting quantum processor, enabling non-linear activation functions for deep learning.

Contribution

It introduces a novel quantum neuron design with non-linear activation using repeat-until-success circuits and constructs a minimal quantum neural network capable of learning Boolean functions.

Findings

Successfully implemented quantum neurons with non-linear activation functions.

Constructed a quantum neural network that learns all 2-to-1-bit Boolean functions.

Demonstrated non-linear classification and learning from superposition states.

Abstract

Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.