Quantum learning of coherent states

TL;DR

This paper introduces a quantum learning method for discriminating coherent states of light used in digital memory reading, demonstrating that joint quantum measurements outperform traditional estimation-based strategies, especially with low-energy signals.

Contribution

It presents a novel quantum learning scheme that uses collective measurements to improve binary state discrimination without entanglement, surpassing Gaussian estimation methods.

Findings

Optimal joint measurements outperform Gaussian estimation strategies.

Collective quantum measurements enhance information readout at low energies.

The approach is effective even without entanglement.

Abstract

We develop a quantum learning scheme for binary discrimination of coherent states of light. This is a problem of technological relevance for the reading of information stored in a digital memory. In our setting, a coherent light source is used to illuminate a memory cell and retrieve its encoded bit by determining the quantum state of the reflected signal. We consider a situation where the amplitude of the states produced by the source is not fully known, but instead this information is encoded in a large training set comprising many copies of the same coherent state. We show that an optimal global measurement, performed jointly over the signal and the training set, provides higher successful identification rates than any learning strategy based on first estimating the unknown amplitude by means of Gaussian measurements on the training set, followed by an adaptive discrimination procedure on the signal. By considering a simplified variant of the problem, we argue that this is the case even for non-Gaussian estimation measurements. Our results show that, even in absence of entanglement, collective quantum measurements yield an enhancement in the readout of classical information, which is particularly relevant in the operating regime of low-energy signals.