Smarter Usage of Measurement Statistics Can Greatly Improve Continuous Variable Quantum Reservoir Computing

TL;DR

This paper introduces two novel methods to enhance the performance of Gaussian states in continuous variable quantum reservoir computing by optimizing measurement usage and incorporating classical memory, leading to improved memory and processing capacity.

Contribution

It proposes innovative measurement and memory strategies that significantly boost Gaussian state performance in quantum reservoir computing, surpassing previous protocols.

Findings

Measurement distribution sampling improves reservoir memory.

Storing past results enhances memory capacity and noise mitigation.

Methods outperform conventional Gaussian state protocols.

Abstract

Quantum reservoir computing is a machine learning scheme in which a quantum system is used to perform information processing. A prospective approach to its physical realization is a photonic platform in which continuous variable (CV) quantum information methods are applied. The simplest CV quantum states are Gaussian states, which can be efficiently simulated classically. As such, they provide a benchmark for the level of performance that non-Gaussian states should surpass in order to give a quantum advantage. In this article we propose two methods to extract more performance from Gaussian states compared to previous protocols. We consider better utilization of the measurement distribution by sampling its cumulative distribution function. We show it provides memory in areas that conventional approaches are lacking, as well as improving the overall processing capacity of the reservoir. We also consider storing past measurement results in classical memory, and show that it improves the memory capacity and can be used to mitigate the effects of statistical noise due to finite measurement ensemble.