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Langmuir monolayers at gas/liquid interfaces provide a rich framework to investigate the interplay between multiscale geometry and mechanics. Monolayer collapse is investigated at a topological and geometric level by building a scale spaceM from experimental imaging data. We present a general lipid monolayer collapse phase diagram, which shows that wrinkling, folding, crumpling, shear banding, and vesiculation are a continuous set of mechanical states that can be approached by either tuning monolayer composition or temperature. The origin of the different mechanical states can be understood by investigating the monolayer geometry at two scales: fluorescent vs atomic force microscopy imaging. We show that an interesting switch in continuity occurs in passing between the two scales, CAFM MAFM 6¼ CFM M. Studying the difference between monolayers that fold vs shear band, we show that shear banding is correlated to the persistence of a multilength scale microstructure within the monolayer at all surface pressures. A detailed analytical geometric formalism to describe this microstructure is developed using the theory of structured deformations. Lastly, we provide the first ever finite element simulation of lipid monolayer collapse utilizing a direct mapping from the experimental image spaceM into a simulation domain P. We show that elastic dissipation inmore »

Finitetemperature phases of manybody quantum systems are fundamental to phenomena ranging from condensedmatter physics to cosmology, yet they are generally difficult to simulate. Using an ion trap quantum computer and protocols motivated by the quantum approximate optimization algorithm (QAOA), we generate nontrivial thermal quantum states of the transversefield Ising model (TFIM) by preparing thermofield double states at a variety of temperatures. We also prepare the critical state of the TFIM at zero temperature using quantum–classical hybrid optimization. The entanglement structure of thermofield double and critical states plays a key role in the study of black holes, and our work simulates such nontrivial structures on a quantum computer. Moreover, we find that the variational quantum circuits exhibit noise thresholds above which the lowestdepth QAOA circuits provide the best results.

Let $G$ be one of the two multigraphs obtained from $K_4e$ by replacing two edges with a doubleedge while maintaining a minimum degree of~2. We find necessary and sufficient conditions on $n$ and $\lambda$ for the existence of a $G$decomposition of $^{\lambda}K_n$.

Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by nearterm quantum computers. Here, we implement a datadriven quantum circuit training algorithm on the canonical BarsandStripes dataset using a quantumclassical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a highdimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.