Beating the classical phase precision limit using a quantum neuromorphic platform

TL;DR

This paper proposes a quantum neuromorphic platform that surpasses classical phase measurement limits by leveraging quantum coherence and entanglement, enabling ultra-precise phase estimation without conditional measurements.

Contribution

It introduces a theoretical model of a quantum network for phase measurement that surpasses classical limits using quantum resources and coherence, without requiring conditional measurements.

Findings

Achieves phase precision beyond the standard quantum limit and Heisenberg limit.

Shows classical mixtures with quantum coherence are sufficient for enhanced measurement.

Compatible with various physical implementations and coupling types.

Abstract

Phase measurement constitutes a key task in many fields of science, both in the classical and quantum regime. The higher precision of such measurement offers significant advances, and can also be utilised to achieve finer estimates for quantities such as distance, the gravitational constant, electromagnetic field amplitude, etc. Here we theoretically model the use of a quantum network, composed of a randomly coupled set of two-level systems, as a processing device for phase measurement. An incoming resource state carrying the phase information interacts with the quantum network, whose emission is trained to produce a desired output signal. We demonstrate phase precision scaling following the standard quantum limit, the Heisenberg limit, and beyond. This can be achieved using quantum resource states such as NOON states or other entangled states, however, we also find that classically correlated mixtures of states are alone sufficient, provided that they exhibit quantum coherence. Our proposed setup does not require conditional measurements, and is compatible with many different types of coupling between the quantum network and the phase encoding state, hence making it attractive to a wide range of possible physical implementations.