Online quantum time series processing with random oscillator networks

TL;DR

This paper introduces a quantum reservoir computing approach for online processing of quantum time series, overcoming measurement challenges and demonstrating tasks involving quantum entanglement and information processing.

Contribution

It proposes a novel quantum reservoir computing method for quantum time series, enabling tasks like entanglement creation without classical analogs and avoiding measurement bottlenecks.

Findings

Successfully generalizes classical benchmarks to quantum data

Demonstrates creation and distribution of entanglement via quantum reservoir

Shows potential for quantum hardware implementation

Abstract

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.