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Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances. 

We describe and interpret the functional morphology of skin of the Yellow‐bellied sea snake,Hydrophis platurus. This is the only pelagic sea snake, and its integument differs from what is known for other species of snakes. In gross appearance, the scales ofH. platurusconsist of non‐overlapping, polygonal knobs with flattened outer surfaces bearing presumptive filamentous sensillae. The deep recesses between scales (‘hinge’) entrap and wick water over the body surface, with mean retention of 5.1 g/cm of skin surface, similar to that determined previously for the roughened, spiny skin of marine file snakes,Acrochordus granulatus. This feature possibly serves to maintain the skin wet when the dorsal body protrudes above water while floating on calm oceanic slicks where they forage. In contrast with other snakes, including three species of amphibious, semi‐marine sea kraits (Laticaudaspp.), the outer corneous β‐protein layer consists of a syncytium that is thinner than seen in most other species. The subjacent α‐layer is also thin, and lipid droplets and lamellar bodies are seen among the immature, cornifying α‐cells. A characteristic mesos layer, comprising the water permeability barrier, is either absent or very thin. These features are possibly related to (1) permeability requirements for cutaneous gas exchange, (2) reduced gradient for water efflux compared with terrestrial environments, (3) less need for physical protection in water compared with terrestrial ground environments, and (4) increased frequency of ecdysis thought to be an anti‐fouling mechanism. The lipogenic features of the α‐layer possibly compensate for the reduced or absent mesos layer, or produce layers of cells that comprise what functionally might be termed a mesos layer, but where the organization of barrier lipids nonetheless appears less robust than what is characteristically seen in squamates.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.