This thesis presents computational studies of three different models of many-body physics with direct or indirect constraints. The presence of constraints in complex many-body systems calls for non-trivial numerical algorithms to study them. The first two models which have a direct form of local constraint are the Rokhsar-Kivelson Quantum Dimer model (QDM) and a classical statistical mechanics model of non-intersecting loops with attractive interactions, both on the square lattice. The investigations of such constrained models have found a recent resurgence with their direct realizations on Rydberg atom arrays quantum simulators. The study of the classical model uses a Monte Carlo directed loop algorithm while the QDM calls for a novel Quantum Monte Carlo scheme based on the framework of Stochastic Series Expansions called the Sweeping Cluster Algorithm (SCA). We present a modification of the SCA in order to render simulations fully ergodic at finite temperature. For both models, our numerical studies show the existence of a critical phase separated by a phase transition at finite temperature to an ordered phase of dimers or loops which spontaneously breaks certain lattice symmetries. We show that for the case where the interaction is attractive this phase transition is of Kosterlitz-Thouless type and can be understood by constructing a coarse-grained field theory through a height mapping. The finite temperature phase diagram of the QDM presents an unusual re-entrance behavior in the critical phase. The final part of this thesis deals with the role of non-abelian symmetries in the thermalization process of quantum many-body systems. We study the high-energy eigenstates of a SU(3) symmetric spin chain in presence of disorder. While the model does not directly have constraints, we perform exact diagonalization in a constrained basis of Young tableau making use of the full SU(3) symmetry of the model. By looking at the commonly used probes for thermalization (spectral statistics, distribution of local observables and scaling of entanglement entropy), we show that the model exhibits a non-ergodic regime over a broad range of system sizes for strong enough disorder, contrasting with the rapid thermalization observed at weak disorder.

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Communication complexity quantifies how difficult it is for two distant computers to evaluate a function f(X,Y), where the strings X and Y are distributed to the first and second computer respectively, under the constraint of exchanging as few bits as possible. Surprisingly, some nonlocal boxes, which are resources shared by the two computers, are so powerful that they allow to collapse communication complexity, in the sense that any Boolean function f can be correctly estimated with the exchange of only one bit of communication. The Popescu-Rohrlich (PR) box is an example of such a collapsing resource, but a comprehensive description of the set of collapsing nonlocal boxes remains elusive. In this work, we carry out an algebraic study of the structure of wirings connecting nonlocal boxes, thus defining the notion of the "product of boxes" P⊠Q, and we show related associativity and commutativity results. This gives rise to the notion of the "orbit of a box", unveiling surprising geometrical properties about the alignment and parallelism of distilled boxes. The power of this new framework is that it allows us to prove previously-reported numerical observations concerning the best way to wire consecutive boxes, and to numerically and analytically recover recently-identified noisy PR boxes that collapse communication complexity for different types of noise models.

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Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulations to reality, using robotics to validate the modeling hypotheses. This challenge arises in bridging what we term the 'biomimicry gap', which is caused by imperfect robotic replicas, communication cues and physics constraints not incorporated in the simulations, that may elicit unrealistic behavioral responses in animals. In this work, we used a biomimetic lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network (NN) model for generating biomimetic social interactions. Through experiments with a biohybrid pair comprising a fish and the robotic lure, a pair of real fish, and simulations of pairs of fish, we demonstrate that our biohybrid system generates social interactions mirroring those of genuine fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal deviation in real-world interactions compared to simulations and fish-only experiments, 2) our NN controls the robot efficiently in real-time, and 3) a comprehensive validation is crucial to bridge the biomimicry gap, ensuring realistic biohybrid systems.

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Using a high-accuracy variational Monte Carlo approach based on group-convolutional neural networks, we obtain the symmetry-resolved low-energy spectrum of the spin-1/2 Heisenberg model on several highly symmetric fullerene geometries, including the famous C60 buckminsterfullerene. We argue that as the degree of frustration is lowered in large fullerenes, they display characteristic features of incipient magnetic ordering: Correlation functions show high-intensity Bragg peaks consistent with Néel-like ordering, while the low-energy spectrum is organized into a tower of states. Competition with frustration, however, turns the simple Néel order into a noncoplanar one. Remarkably, we find and predict chiral incipient ordering in a large number of fullerene structures.

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