Neural Networks for Detecting Multimode Wigner Negativity

Abstract

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here, we introduce a new technique, based on a machine learning protocol with artificial neural networks, that allows us to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate, and more robust than conventional methods when limited amounts of data are available. Moreover, the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

“…These are capable of handling large data sets and of solving tasks for which they have not been explicitly programmed; applications range from stock-price predictions [11,12] to the analysis of medical diseases [13]. In the past few years, several applications of machine-learning methods in the quantum domain have been reported [14][15][16], including state and unitary tomography [17][18][19][20][21][22][23][24][25], the design of quantum experiments [26][27][28][29][30][31][32], the validation of quantum technology [33][34][35], the identification of quantum features [36,37], and the adaptive control of quantum devices [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Also, photonic platforms can be exploited for the realization of machine-learning protocols [55,56]...…”

Experimental Quantum Embedding for Machine Learning

“…This comes in the framework of an exchange of concepts and methods between machine learning and quantum information [11]. Indeed, there have been demonstrations of the benefits of machine learning approaches to analyse data generated by quantum experiments [12][13][14][15][16][17][18][19], to improve the performance of quantum sensors [20,21], to Bayesian parameter estimations [22], to the classification of non-Markovian noise [23], and to the design of optical experiments [24,25]. Small gate-model devices and quantum annealers have been used to perform quantum heuristic optimization [26][27][28][29][30] and to solve classification problems [31][32][33][34].…”

Identifying nonclassicality from experimental data using artificial neural networks

“…In recent years, the problem of the classification of unstructured and complex data was increasingly addressed with the help of machine learning techniques [34]. In the quantum domain, a wide range of challenges was tackled using various different forms of machine learning, see, e.g., [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and Ref. [53] for a review.…”