Direct implementation of a perceptron in superconducting circuit quantum hardware

TL;DR

This paper demonstrates a superconducting circuit implementation of a quantum perceptron, enabling controllable activation functions and multi-qubit entangling operations, paving the way for advanced quantum neural networks.

Contribution

It introduces a direct superconducting hardware implementation of a quantum perceptron with tunable parameters and multi-qubit entangling capabilities, advancing quantum neural network development.

Findings

Successful realization of a superconducting quantum perceptron

Tunable activation function, input weight, and bias

Efficient multi-qubit entangling operation in a single step

Abstract

The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in [Torrontegui and Garc\'ia-Ripoll 2019 EPL 125 30004], we demonstrate a superconducting qubit implementation of an adiabatic controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. We show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the adiabatic gate length, the coupling between the qubits and the frequency of the applied drive, respectively. In its general form, the gate realizes a multi-qubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.