Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement

TL;DR

This paper introduces a quantum neuromorphic sensing platform that learns to detect and quantify entanglement, including gravity-induced entanglement between masses, with precision surpassing traditional methods.

Contribution

The authors propose a novel quantum neuromorphic network for sensing entanglement, capable of learning to recognize quantum states and outperforming direct measurement techniques in precision.

Findings

The platform can sense entanglement after training with non-entangled states.

It achieves measurement precision beyond the standard quantum limit.

It predicts a two-order-of-magnitude improvement in gravity-induced entanglement detection.

Abstract

The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is trained to learn and sense the entanglement of the former. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognise quantum states, it is possible to sense the amount of entanglement after training with only non-entangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilise our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques.