Quantum reservoir neural network implementation on coherently coupled quantum oscillators

TL;DR

This paper presents a novel quantum reservoir computing implementation using parametrically coupled quantum oscillators, achieving high accuracy with fewer components than classical counterparts, and enabling scalable quantum neural networks.

Contribution

The paper introduces a new quantum reservoir architecture based on coupled quantum oscillators, surpassing qubit-based systems in connectivity and scalability.

Findings

Achieved 99% accuracy on benchmark tasks.

Created a reservoir with up to 81 neurons using only two oscillators.

Demonstrated potential for large-scale quantum neural networks with minimal hardware.

Abstract

Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyse a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99 % on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators.