Tamás Kriváchy

In the last decade, it was understood that quantum networks involving several independent sources of entanglement which are distributed and measured by several parties allowed for completely novel forms of nonclassical quantum correlations, when entangled measurements are performed. Here, we experimentally obtain quantum correlations in a triangle network structure and provide solid evidence of its nonlocality. Specifically, we first obtain the Elegant distribution proposed in [Gisin et al., Entanglement 25 years after quantum teleportation: Testing joint measurements in quantum networks, Entropy 21, 325 (2019)ENTRFG1099-430010.3390/e21030325] by performing a six-photon experiment. Then, we justify its nonlocality based on machine learning tools to estimate the distance of the experimentally obtained correlation to the local set.

We show that citation metrics of journal articles in many of the online-only Springer Nature journals and associated ones are distorted, going back to articles from 2001. We find that most likely due to an API response error, there are many incorrect references which typically lead to Article Number 1 of a given Volume. Among others, the issue affects journals such as Scientific Reports, Nature Communications, Communications journals, Cell Death & Disease, Light: Science & Applications, as well as many BMC, Discovery and npj journals. Beyond the negative effect of introducing incorrect reference information, this distorts the citation statistics of articles in these journals, with a few articles being massively over-cited compared to their peers, while many lose citations; e.g. both in Scientific Reports and in Nature Communications, 5 of the 10 top cited articles have article numbers of 1. We validate the distorted statistics by assessing data from multiple scientific literature databases: Crossref, OpenCitations, Semantic Scholar, and the journals' websites. The issue primarily arises from the inconsistent transition from page-based referencing of articles to article number-based referencing, as well as the improper handling of the change in the publisher's article metadata API. It seems that the most pressing problem has been present since approximately 2011, which we estimate affects the citation count of millions of authors.

Bell nonlocality without input settings, .g., in the triangle network, has been perceived to be particularly fragile, with low robustness to noise in physical implementations. Here, we show to the contrary that nonlocality based on N00N states already for N=2 has an exceptionally high robustness to photon loss. For the dominant noise factor, single photon loss in the transmission channels, we can certify noise robustness up to 10% loss, while for a realistic noise model we use neural network-based heuristics to observe ∼50% robustness. Moreover we show that the robustness holds even for imperfect sources based on spontaneous parametric down-conversion sources, where the heralding information of the sources can be used to avoid any global postprocessing of the outcomes, such as discarding rounds when photons fail to arrive, and thus demonstrate how the detection loophole in the triangle network can be closed.

Characterizing the set of distributions that can be realized in the triangle network is a notoriously difficult problem. In this work, we investigate inner approximations of the set of local (classical) distributions of the triangle network. A quantum distribution that appears to be nonlocal is the elegant joint measurement (EJM) [], which motivates us to study distributions having the same symmetries as the EJM. We compare analytical and neural-network-based inner approximations and find a remarkable agreement between the two methods. Using neural network tools, we also conjecture network Bell inequalities that give a trade-off between the levels of correlation and symmetry that a local distribution may feature. Our results considerably strengthen the conjecture that the EJM is nonlocal. Published by the American Physical Society 2025

We discuss quantum network Bell nonlocality in a setting where the network structure is not fully known. More concretely, an honest user may trust their local network topology, but not the structure of the rest of the network, involving distant (and potentially dishonest) parties. We demonstrate that quantum network nonlocality can still be demonstrated in such a setting, hence exhibiting topological robustness. Specifically, we present quantum distributions obtained from a simple network that cannot be reproduced by classical models, even when the latter are based on more powerful networks. In particular, we show that in a large ring network, the knowledge of only a small part of the network structure (involving only two or three neighboring parties) is enough to guarantee nonlocality over the entire network. This shows that quantum network nonlocality can be extremely robust to changes in the network topology. Moreover, we demonstrate that applications of quantum nonlocality, such as the black-box certification of randomness and entanglement, are also possible in such a setting.

We investigate network nonlocality in the triangle scenario when all three parties have no input and binary outputs. Through an explicit example, we prove that this minimal scenario supports nonlocal correlations compatible with no-signaling and independence of the three sources, but not with realisations based on independent quantum or classical sources. This nonlocality is robust to noise. Moreover, we identify the equivalent to a Popescu-Rohrlich box in the minimal triangle scenario.

Achieving spin squeezing within solid-state devices is a long-standing research goal due to the promise of their particularities, for instance, their long coherence times, the possibility of low-temperature experiments, or the integration of entanglement-assisted sensors on-chip. In this work, we investigate an interferometer-free four-color scheme to achieve spin squeezing of rare-earth-ion-doped crystals. The proposal relies on an analytic derivation that starts from a Tavis-Cummings model for light-matter interaction, providing microscopic insights onto spin-squeezing generation. We evince the spin-squeezing signature in the light intensity variance. We consider the two particular cases of europium- and praseodymium-doped yttrium orthosilicates, workhorses of quantum technology developments. We show that up to 8 dB of spin squeezing can be obtained with readily accessible experimental resources, including noise due to photon scattering. Our results for rare-earth-ion-doped crystals add to the promising properties of these platforms for manipulating many-body entangled states and for high-precision measurements.

A single-photon maximally entangled state is obtained when a photon impinges on a balanced beamsplitter. Its nonlocal properties have been intensively debated in the quantum optics and foundations communities. It is however clear that a standard Bell test made only of passive optical elements cannot reveal the nonlocality of this state. We show that the nonlocality of single-photon entangled states can nevertheless be revealed in a quantum network made only of beamsplitters and photodetectors. In our protocol, three single-photon entangled states are distributed in a triangle network, introducing indeterminacy in the photons' paths and creating nonlocal correlations without the need for measurements choices. We discuss a concrete experimental realization and provide numerical evidence of the tolerance of our protocol to standard noise sources. Our results show that single-photon entanglement may constitute a promising solution to generate genuine network-nonlocal correlations useful for Bell-based quantum information protocols.

Semidefinite programming is an important optimization task, often used in time-sensitive applications. Though they are solvable in polynomial time, in practice they can be too slow to be used in online, i.e. real-time applications. Here we propose to solve feasibility semidefinite programs using artificial neural networks. Given the optimization constraints as an input, a neural network outputs values for the optimization parameters such that the constraints are satisfied, both for the primal and the dual formulations of the task. We train the network without having to exactly solve the semidefinite program even once, thus avoiding the possibly time-consuming task of having to generate many training samples with conventional solvers. The neural network method is only inconclusive if both the primal and dual models fail to provide feasible solutions. Otherwise we always obtain a certificate, which guarantees false positives to be excluded. As a proof-of-principle demonstration, we examine the performance of the method on a hierarchy of quantum information tasks, the Navascués–Pironio–Acín hierarchy applied to the Bell scenario. We demonstrate that the trained neural network gives decent accuracy, while showing orders of magnitude increase in speed compared to a traditional solver, once trained. The network does not have to be retrained for similarly structured problems, giving the promise for being a fast solver for these types of problems.