Impact of the form of weighted networks on the quantum extreme reservoir computation

TL;DR

This paper investigates how the structure of weighted networks influences the quantum feature maps in quantum reservoir computing, proposing a disordered discrete time crystal model for efficient implementation and high performance.

Contribution

It introduces a method to characterize quantum feature maps via weighted networks and demonstrates a simple Hamiltonian model for near-optimal quantum reservoir performance.

Findings

Weighted network models effectively characterize quantum feature maps.

Disordered discrete time crystal Hamiltonian achieves high performance with simple implementation.

Quantum reservoir performance improves with increased time-step dynamics.

Abstract

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance while removing the necessity of programming of the quantum processor gate by gate.