Abstract

Creating large-scale entanglement lies at the heart of many quantum information processing protocols and the investigation of fundamental physics. For multipartite quantum systems, it is crucial to identify not only the presence of entanglement but also its detailed structure. This is because in a generic experimental situation with sufficiently many subsystems involved, the production of so-called genuine multipartite entanglement remains a formidable challenge. Consequently, focusing exclusively on the identification of this strongest type of entanglement may result in an all or nothing situation where some inherently quantum aspects of the resource are overlooked. On the contrary, even if the system is not genuinely multipartite entangled, there may still be many-body entanglement present in the system. An identification of the entanglement structure may thus provide us with a hint about where imperfections in the setup may occur, as well as where we can identify groups of subsystems that can still exhibit strong quantum-information-processing capabilities. However, there is no known efficient methods to identify the underlying entanglement structure. Here, we propose two complementary families of witnesses for the identification of such structures. They are based on the detection of entanglement intactness and entanglement depth, each requires only the implementation of solely two local measurements. Our method is also robust against noises and other imperfections, as reflected by our experimental implementation of these tools to verify the entanglement structure of five different eight-photon entangled states. We demonstrate how their entanglement structure can be precisely and systematically inferred from the experimental data. In achieving this goal, we also illustrate how the same set of data can be classically postprocessed to learn the most about the measured system.

Highlights

  • We introduce the notion of an entanglement structure, which details the extent of many-body entanglement present and their segregation among the various subsystems

  • The retrieval of any partial information on the entanglement structure of an experimentally prepared system is always welcome, as it provides diagnostic information on where imperfections in the setup may lie. Such information is often already available in the data collected for the measurement of entanglement witnesses, even if the measured value does not reveal genuine multipartite entanglement

  • By introducing some auxiliary free parameters, one can, in principle, always optimize the choice of the witness depending on the measured data, as we illustrate in Sec

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Summary

INTRODUCTION

Entanglement [1], one of the defining features offered by quantum theory, is known to be an essential resource in many quantum information processing tasks, including quantum computing [2], quantum cryptography [3,4], quantum. To overcome imperfections in the preparation procedure, it would be crucial to identify the extent to which the entanglements produced are segregated, as captured by the nonseparability [1] of the state. The identification of such entanglement structures is generally challenging, especially when full state reconstruction is infeasible. Each family of witnesses involves the same local measurement regardless of the number of subsystems present They do not depend on the extent of nonseparability or entanglement depth to be certified—this follows directly from the extent to which the respective witnesses are violated. As an illustration of how these witnesses fare in practice, we experimentally prepare several eight-photon quantum states and demonstrate how the measurement of these two families of EWs—which involves altogether the measurement of four distinct local observables—enable us to infer nontrivial information about the underlying entanglement structure

ENTANGLEMENT STRUCTURE
EXPERIMENTAL REALIZATION
PBS3 2 5 7 3 4
CONCLUSION
Entanglement preparation
Imperfections and noise model
Algorithmic procedure to deduce a minimal entanglement structure
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