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The quantum mechanics formalism introduced new revolutionary concepts challenging our everyday perceptions. Arguably, quantum entanglement, which explains correlations that cannot be reproduced classically, is the most notable of them. Besides its fundamental aspect, entanglement is also a resource, fueling emergent technologies such as quantum simulators and computers. The purpose of this chapter is to give a pedagogical introduction to the topic with a special emphasis on the multipartite scenario, i.e., entanglement distributed among many degrees of freedom. Due to the combinatorial complexity of this setting, particles can interact and become entangled in a plethora of ways, which we characterize here. We start by providing the necessary mathematical tools and elementary concepts from entanglement theory. A part of this chapter will be devoted to classifying and ordering entangled states. Then, we focus on various entanglement structures useful in condensed-matter theory such as tensor-network states or symmetric states useful for quantum-enhanced sensing. Finally, we discuss state-of-the-art methods to detect and certify such correlations in experiments, with some relevant illustrative examples., Comment: 61 pages, 8 figures. Final version of the chapter in the book "New Trends and Platforms for Quantum Technologies", Lecture Notes in Physics, Springer

An optimal local quantum thermometer is a quantum many-body system that saturates the fundamental lower bound for the thermal state temperature estimation accuracy [L. Correa, et. al., Phys. Rev. Lett. 114, 220405 (2015)]. Such a thermometer has a particular energy level structure with a single ground state and highly degenerated excited states manifold, with an energy gap proportional to the estimated temperature. In this work, we show that the optimal local quantum thermometer can be realized in an experimentally feasible system of spinless fermions confined in a one-dimensional optical lattice described by the Rice-Mele model. We characterize the system's sensitivity to temperature changes in terms of quantum Fisher information and the classical Fisher information obtained from experimentally available site occupation measurements., Comment: 5 figures

Resource-efficient quantum state tomography is one of the key ingredients of future quantum technologies. In this work, we propose a new tomography protocol combining standard quantum state reconstruction methods with an attention-based neural network architecture. We show how the proposed protocol is able to improve the averaged fidelity reconstruction over linear inversion and maximum-likelihood estimation in the finite-statistics regime, reducing at least by an order of magnitude the amount of necessary training data. We demonstrate the potential use of our protocol in physically relevant scenarios, in particular, to certify metrological resources in the form of many-body entanglement generated during the spin squeezing protocols. This could be implemented with the current quantum simulator platforms, such as trapped ions, and ultra-cold atoms in optical lattices., Comment: The Author Accepted Manuscript (AAM)

We introduce a semidefinite programming algorithm to find the minimal quantum Fisher information compatible with an arbitrary dataset of mean values. This certification task allows one to quantify the resource content of a quantum system for metrology applications without complete knowledge of the quantum state. We implement the algorithm to study quantum spin ensembles. We first focus on Dicke states, where our findings challenge and complement previous results in the literature. We then investigate states generated during the one-axis twisting dynamics, where in particular we find that the metrological power of the so-called multi-headed cat states can be certified using simple collective spin observables, such as fourth-order moments for small systems, and parity measurements for arbitrary system sizes., Comment: 26 pages, 10 figures. Accepted version in Quantum Journal

Many concurrent dictionary implementations are designed and optimized for read-mostly workloads with uniformly distributed keys, and often perform poorly on update-heavy workloads. In this work, we first present a concurrent (a,b)-tree, the OCC-ABtree, which outperforms its fastest competitor by up to 2x on uniform update-heavy workloads, and is competitive on other workloads. We then turn our attention to skewed update-heavy workloads (which feature many inserts/deletes on the same key) and introduce the Elim-ABtree, which uses a new optimization called publishing elimination. In publishing elimination, concurrent inserts and deletes to a key are reordered to eliminate them. This reduces the number of writes in the data structure. The Elim-ABtree achieves up to 2.5x the performance of its fastest competitor (including the OCC-ABtree). The OCC-ABtree and Elim-ABtree are linearizable. We also introduce durable linearizable versions (for systems with Intel Optane DCPMM non-volatile main memory) that are nearly as fast., Comment: 22 pages, 17 figures, 1 table. Full version of the paper to published in Principles and Practice of Parallel Programming (PPoPP) 2022