Learning to detect optical nonclassicality

TL;DR

This paper introduces a data-driven, interpretable machine learning approach to detect quantum nonclassicality in optical states using limited measurement data, improving robustness over traditional methods.

Contribution

It develops a variational model trained on experimental data to identify nonclassical states, tailored to specific measurement schemes and finite data scenarios.

Findings

Successfully trained on superconducting nanowire detector data

Effective detection of nonclassicality with limited samples

Versatile approach applicable to different photon detection schemes

Abstract

Nonclassicality, defined in the quantum optical sense, serves as a resource for photon-based quantum technologies. Therefore, certifying the nonclassicality of a quantum state is crucial for gauging its potential for quantum advantage. However, traditional nonclassicality witnesses that assume perfect knowledge of the witness observables often fail in realistic scenarios with limited statistics and finite-resolution photon detectors. Furthermore, these witnesses do not exploit the fact that certain states are unlikely to be observed in a given experiment. Here, we train a variational model to distinguish classical from nonclassical states using finitely many measurement samples of multimode quantum states that are probed with different photon-number-resolving detection schemes. The learned decision rule is then an indicator of nonclassicality, tailored to a given set of physically relevant states. Our approach is both data-driven and interpretable in the sense that the learned analytical decision rule can be extracted. Training the model on experimental data measured with (i) a superconducting nanowire single-photon detector and (ii) a time-bin multiplexing detection scheme demonstrates the versatility of the approach, paving the way for efficient nonclassicality detection.