Optimizing a quantum reservoir computer for time series prediction

TL;DR

This paper investigates how to optimize a quantum reservoir computer based on the transverse field Ising model for improved time series prediction by tuning interactions and timescales, demonstrating enhanced memory capacity and real-world application.

Contribution

It introduces methods to enhance quantum reservoir computing performance through interaction engineering and timescale optimization, linking physical properties to computational accuracy.

Findings

Variation in inter-spin interactions improves memory capacity.

Optimal timescales maximize memory performance.

Faster decay of out-of-time-ordered correlator correlates with better memory.

Abstract

Quantum computing and neural networks show great promise for the future of information processing. In this paper we study a quantum reservoir computer (QRC), a framework harnessing quantum dynamics and designed for fast and efficient solving of temporal machine learning tasks such as speech recognition, time series prediction and natural language processing. Specifically, we study memory capacity and accuracy of a quantum reservoir computer based on the fully connected transverse field Ising model by investigating different forms of inter-spin interactions and computing timescales. We show that variation in inter-spin interactions leads to a better memory capacity in general, by engineering the type of interactions the capacity can be greatly enhanced and there exists an optimal timescale at which the capacity is maximized. To connect computational capabilities to physical properties of the underlaying system, we also study the out-of-time-ordered correlator and find that its faster decay implies a more accurate memory. Furthermore, as an example application on real world data, we use QRC to predict stock values.