Early stages of radiation damage in graphite and carbon nanostructures: A first-principles molecular dynamics study

Understanding radiation-induced defect formation in carbon materials is crucial for nuclear technology and for the manufacturing of nanostructures with desired properties. Using first principles molecular dynamics, we perform a systematic study of the non-equilibrium processes of radiation damage in graphite. Our study reveals a rich variety of defect structures (vacancies, interstitials, intimate interstitial-vacancy pairs, and in-plane topological defects) with formation energies of 5--15 eV. We clarify the mechanisms underlying their creation and find unexpected preferences for particular structures. Possibilities of controlled defect-assisted engineering of nanostructures are analyzed. In particular, we conclude that the selective creation of two distinct low-energy intimate Frenkel pair defects can be achieved by using a 90--110 keV electron beam irradiation.Comment: 5 pages, 4 figure

Phenomenological theory of variational quantum ground-state preparation

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting parametrized quantum circuits that may offer an advantage compared to classical trial states used, for instance, in quantum Monte Carlo or tensor network calculations. While, traditionally, the main focus has been on developing better trial circuits, we show that the algorithm's success, if optimized within stochastic gradient descent (SGD) or quantum natural gradient descent (QNGD), crucially depends on other parameters such as the learning rate, the number Ns of measurements to estimate the gradient components, and the Hamiltonian gap Δ. Within the standard SGD or QNGD, we first observe the existence of a finite Ns value below which the optimization is impossible, and the energy variance resembles the behavior of the specific heat in second-order phase transitions. Second, when Ns is above such threshold level, and learning is possible, we develop a phenomenological model that relates the fidelity of the state preparation with the optimization hyperparameters and Δ. More specifically, we observe that the computational resources scale as 1/Δ2, and we propose a symmetry enhancement of the variational ansatz as a way to increase the closing gap. We test our understanding on several instances of two-dimensional frustrated quantum magnets, which are believed to be the most promising candidates for near-term quantum advantage through variational quantum simulations