Search

In recent years, levitated optomechanics has delivered on the promise of reaching the motional quantum ground state. An important next milestone of the field would be the generation of mechanical entanglement. An ideal candidate is the two-dimensional motion in the polarization plane of an optical tweezer inside an optical cavity, where optical and mechanical modes are coupled via coherent scattering. Necessary conditions towards this achievement are two-dimensional ground state cooling along with substantial spectral overlap between the two modes. The latter condition is required to generate the necessary correlations, but unfortunately, it hinders efficient cooling thus narrowing the useful parameter space. In this work, we report the achievement of a high purity two-dimensional state in a regime where the strong optomechanical coupling induces the desired spectral superposition between oscillations in different directions, as reflected in the non-trivial spectral shape of the detected cavity field. As a result, significant correlations consistently arise between any pair of orthogonal directions, preventing the motion from being reduced to two independent one-dimensional oscillators and leading to higher purity compared to that scenario. Our system serves as an excellent platform for realizing continuous variable entanglement in two-dimensional motion.

Levitated optomechanics is entering the multiparticle regime, paving the way for the use of arrays of strongly coupled massive oscillators to explore complex interacting quantum systems. Here, we demonstrate the trapping of two nanospheres inside a dual optical tweezer generated by two copropagating lasers operating at different wavelengths (1064 nm and 976 nm). Due to the chromatic aberration of the tweezer optics, two focal points are created approximately 9 microns apart, each one acting as an optical trap for a silica nanoparticle. At this distance, the surface charges on the nanospheres produce a Coulomb force that couples their motion along the tweezer axis. The strong coupling regime is achieved, as evidenced by the observed avoided crossing of the normal-mode frequencies. These results highlight the potential of our experimental scheme for future studies on systems of strongly coupled oscillators, including their implementation in optical cavities, both in the classical and in quantum regime.

This paper studies the problem of increasing the connectivity of an ad-hoc peer-to-peer network subject to cyber-attacks targeting the agents in the network. The adopted strategy involves the design of local interaction rules for the agents to locally modify the graph topology by adding and removing links with neighbors. Two distributed protocols are presented to boost the algebraic connectivity of the network graph beyond $k-2\sqrt{k-1}$ where $k\in \mathbb{N}$ is a free design parameter; these two protocols are achieved through the distributed construction of random (approximate) regular graphs. One protocol leverages coordinated actions between pairs of neighboring agents and is mathematically proven to converge to the desired graph topology. The other protocol relies solely on the uncoordinated actions of individual agents and it is validated by a spectral analysis through Monte-Carlo simulations. Numerical simulations offer a comparative analysis with other state-of-the-art algorithms, showing the ability of both proposed protocols to maintain high levels of connectivity despite attacks carried out with full knowledge of the network structure, and highlighting their superior performance.

Single molecules trapped in the solid state at liquid helium temperatures are promising quantum emitters for the development of quantum technologies owing to their remarkable photostability and their lifetime-limited optical coherence time of the order of 10 ns. The coherent preparation of their electronic state requires resonant excitation with a Rabi period much shorter than their optical coherence time. Sculpting the optical excitation with sharp edges and a high on-off intensity ratio ($\sim 3\times 10^5$) from a single-frequency laser beam, we demonstrate sub-nanosecond drive of a single dibenzanthanthrene molecule embedded in a naphthalene matrix at 3.2$^\circ$K, over more than 17 Rabi periods. With pulses tailored for a half-Rabi period, the electronic excited state is prepared with fidelity as high as 0.97. Using single-molecule Ramsey spectroscopy, we prove up to 5$^\circ$K that the optical coherence lifetime remains at its fundamental upper limit set by twice the excited-state lifetime, making single molecules suitable for quantum bit manipulations under standard cryogen-free cooling technologies.

The recent deployment of multi-agent networks has enabled the distributed solution of learning problems, where agents cooperate to train a global model without sharing their local, private data. This work specifically targets some prevalent challenges inherent to distributed learning: (i) online training, i.e., the local data change over time; (ii) asynchronous agent computations; (iii) unreliable and limited communications; and (iv) inexact local computations. To tackle these challenges, we apply the Distributed Operator Theoretical (DOT) version of the Alternating Direction Method of Multipliers (ADMM), which we call "DOT-ADMM". We prove that if the DOT-ADMM operator is metric subregular, then it converges with a linear rate for a large class of (not necessarily strongly) convex learning problems toward a bounded neighborhood of the optimal time-varying solution, and characterize how such neighborhood depends on (i)-(iv). We first derive an easy-to-verify condition for ensuring the metric subregularity of an operator, followed by tutorial examples on linear and logistic regression problems. We corroborate the theoretical analysis with numerical simulations comparing DOT-ADMM with other state-of-the-art algorithms, showing that only the proposed algorithm exhibits robustness to (i)-(iv).

In this work, we characterize a class of nonlinear monotone dynamical systems that have a certain translation invariance property which goes by the name of plus-homogeneity; usually called "topical" systems. Such systems need not be asymptotically stable, since they are merely nonexpansive but not contractive. Thus, we introduce a stricter version of monotonicity, termed "type-K" in honor of Kamke, and we prove the asymptotic stability of the equilibrium points, as well as the convergence of all trajectories to such equilibria for type-K monotone and plus-homogeneous systems: we call them "K-topical". Since topical maps are the natural nonlinear counterpart of linear maps defined by row-stochastic matrices, which are a cornerstone in the convergence analysis of linear multi-agent systems (MASs), we exploit our results for solving the consensus problem over nonlinear K-topical MASs. We first provide necessary and sufficient conditions on the local interaction rules of the agents ensuring the K-topicality of a MAS. Then, we prove that the agents achieve consensus asymptotically if the graph describing their interactions contains a globally reachable node. Finally, several examples for continuous-time and discrete-time systems are discussed to corroborate the enforceability of our results in different applications.

We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (M$^{pro}$) inhibitors whose potency range from mM for initial non-covalent ligands to sub-$\mu$M for the final covalent compound (IC50=830 +/- 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces. Machine Learning predictions are also performed to predict selected compound properties. While simulations extensively use High Performance Computing to strongly reduce time-to-solution, they were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and to in vitro characterization of compounds. Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem. The proposed fluorinated tetrahydroquinolines open routes for further optimization of M$^{pro}$ inhibitors towards low nM affinities.

The control and manipulation of quantum-entangled non-local states is a crucial step for the development of quantum information processing. A promising route to achieve such states on a wide scale is to couple solid-state quantum emitters through their coherent dipole-dipole interactions. Entanglement in itself is challenging, as it requires both nanometric distances between emitters and nearly degenerate electronic transitions. Implementing hyperspectral imaging to identify pairs of coupled organic molecules trapped in a low temperature matrix, we reach distinctive spectral signatures of maximal molecular entanglement by tuning the optical resonances of the quantum emitters by Stark effect. We also demonstrate far-field selective excitation of the long-lived subradiant delocalized states with a laser field tailored in amplitude and phase. Interestingly, optical nanoscopy images of the entangled molecules unveil novel spatial signatures that result from quantum interferences in their excitation pathways and reveal the exact locations of each quantum emitter. Controlled molecular entanglement can serve as a test-bench to decipher more complex physical or biological mechanisms governed by the coherent coupling and paves the way towards the realization of new quantum information processing platforms.

In this paper we propose a novel consensus protocol for discrete-time multi-agent systems (MAS), which solves the dynamic consensus problem on the max value, i.e., the dynamic max-consensus problem. In the dynamic max-consensus problem to each agent is fed a an exogenous reference signal, the objective of each agent is to estimate the instantaneous and time-varying value of the maximum among the signals fed to the network, by exploiting only local and anonymous interactions among the agents. The absolute and relative tracking error of the proposed distributed control protocol is theoretically characterized and is shown to be bounded and by tuning its parameters it is possible to trade-off convergence time for steady-state error. The dynamic Max-consensus algorithm is then applied to solve the distributed size estimation problem in a dynamic setting where the size of the network is time-varying during the execution of the estimation algorithm. Numerical simulations are provided to corroborate the theoretical analysis.

In this paper we propose a novel method to establish stability and, in addition, convergence to a consensus state for a class of discrete-time Multi-Agent System (MAS) evolving according to nonlinear heterogeneous local interaction rules which is not based on Lyapunov function arguments. In particular, we focus on a class of discrete-time MASs whose global dynamics can be represented by sub-homogeneous and order-preserving nonlinear maps. This paper directly generalizes results for sub-homogeneous and order-preserving linear maps which are shown to be the counterpart to stochastic matrices thanks to nonlinear Perron-Frobenius theory. We provide sufficient conditions on the structure of local interaction rules among agents to establish convergence to a fixed point and study the consensus problem in this generalized framework as a particular case. Examples to show the effectiveness of the method are provided to corroborate the theoretical analysis.