Learning functions of quantum states with distributed architectures

TL;DR

This paper investigates distributed quantum machine learning architectures for efficiently learning properties of quantum states, demonstrating scalability and resource reduction for complex nonlinear features through entanglement and multiplexing.

Contribution

It introduces a novel distributed design for quantum property learning that reduces resource requirements and enhances scalability by leveraging entanglement and multiple subsystems.

Findings

Distributed architecture reduces resource needs for linear tasks.

Entanglement-based design improves nonlinear property reconstruction.

Scalable approach enables learning complex quantum features efficiently.

Abstract

Distributed architectures are gaining prominence in quantum machine learning as a means to overcome hardware limitations and enable scalable quantum information processing. In this context, we analyze the design and performance of distributed Quantum Extreme Learning Machine (QELM) architectures for learning functions of quantum states directly from data, restricting measurements to easily implementable projective measurements in the computational basis. The aim is to determine which schemes can effectively recover specific properties of input quantum states, including both linear and nonlinear features, while also quantifying the resource requirements in terms of measurements and reservoir dimensionality. We compare standard three-layer QELM with a spatially multiplexed architecture composed of multiple independent three-layer units for linear (quantum) tasks, showing a linear reduction in resource requirements per unit. For nonlinear properties, the study examines the multiple-injection architecture and introduces a novel distributed design that incorporates entanglement between subsystems within a spatially multiplexed framework, evaluating its performance through the reconstruction of complex nonlinear quantities such as polynomial targets, R\'enyi entropy, and entanglement measures. Our results demonstrate that the distributed design enables the reconstruction of higher-order nonlinearities by increasing the number of interacting subsystems with reduced resources, rather than increasing the size of an individual reservoir, providing a scalable and hardware-efficient route to quantum property learning.