Towards a feasible implementation of quantum neural networks using quantum dots

TL;DR

This paper proposes a feasible approach to implementing quantum neural networks using quantum dots, demonstrating sustained quantum coherence at relatively high temperatures, which advances practical quantum computing.

Contribution

It introduces a novel quantum dot-based architecture for neural networks and shows its viability through detailed numerical simulations at accessible temperatures.

Findings

Quantum coherence persists over 100 ps at 77 K.

Implementation is feasible with GaAs quantum dots.

Coherence duration is three orders of magnitude higher than SQUID systems.

Abstract

We propose an implementation of quantum neural networks using an array of quantum dots with dipole-dipole interactions. We demonstrate that this implementation is both feasible and versatile by studying it within the framework of GaAs based quantum dot qubits coupled to a reservoir of acoustic phonons. Using numerically exact Feynman integral calculations, we have found that the quantum coherence in our neural networks survive for over a hundred ps even at liquid nitrogen temperatures (77 K), which is three orders of magnitude higher than current implementations which are based on SQUID-based systems operating at temperatures in the mK range.