A method to self-test multipartite quantum states is constructed and shows that it is possible to characterize quantum states based only on observed statistics and without knowing any detail of the inner mechanism or the physical dimension of the Hilbert space of the system.
Self-testing is the strongest certification procedure that uniquely characterizes the physical system based on the observed statistics, without any knowledge of the inner workings of the devices. The optimal quantum violation of a Bell inequality enables such a device-independent (DI) self-testing of the source and the measurement devices. In this work, we demonstrate the DI self-testing based on the arbitrary-input chained Bell inequality. We devise a systematic and elegant sum-of-squares (SOS) technique enabling dimension-independent optimization of the quantum violation. Our approach enables the derivation of the state along with the relationship between the local observables directly from the optimization condition. One significant aspect is the robustness of such self-testing in real experimental situations involving noise and imperfection, leading to deviation from the optimal quantum violation. We provide an analytical technique for robust self-testing in the presence of noise. As an application of our scheme, we demonstrate the generation of two bit DI randomness and analyze the robustness of such randomness. Our optimization method is both simple and elegant, making it suitable for deriving the optimal quantum violation of various arbitrary-input Bell inequalities.
Bell nonlocality provides the foundation for device-independent (DI) certification of quantum devices. We introduce a Bell inequality capable of identifying genuine multipartite nonlocality (GMNL) in an arbitrary m-partite scenario with an arbitrary odd number of measurements per party. Since the multi-setting nature of this inequality precludes the use of Jordan's Lemma, we construct an analytical sum-of-squares (SOS) decomposition to obtain the optimal quantum violation without assuming any bound on the Hilbert space dimension. This, in turn, enables self-testing of the shared entangled state and the corresponding measurement observables, up to local isometries, whose existence we confirm using a swap-based certification scheme. In addition, we show that our framework enables the extraction of maximal global DI randomness (m bits) at the optimal quantum violation, thereby exceeding previous limitations in the GMNL regime. Finally, we demonstrate that the architecture of our inequality yields improved robustness to noise as the number of measurement settings grows, ensuring experimental feasibility.
Quantum entanglement, has been acknowledged as a precious resource due to its inherent nonclassical correlations between subsystems. These quantum correlations have the potential for many quantum processes, including canonical ones: quantum cryptography, quantum teleportation, and dense coding. To exploit the advantages of quantum entanglement, two essential premises are required, i.e., to prepare high-quality entanglement and characterize quality level of prepared entanglement. Thus far, quantum entanglement can be produced in various quantum systems; however, it appears that this new resource is complex and difficult to characterize. The standard methods to characterize multipartite entanglement, e.g., entanglement witness, state tomography, or quantum state verification, require full knowledge of the Hilbert space dimension and precise calibration of measurement devices, which are usually difficult to acquire in experiment. The most radical way to overcome these problems is to detect entanglement solely based on the Bell-like correlations of measurement outcomes collected in the experiment, namely, device-independent characterization of entanglement. This article reviews the recently developed device-independent methods to characterize entanglement, including self-testing and device-independent certification of entanglement. These approaches can be widely applied in kinds of quantum information processing, especially for those with security demands.