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Lasse Gerblich

Publications and source records attributed to Lasse Gerblich.

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Evolution of Cosmic String Loops under Gravitational Backreaction

Nambu-Goto cosmic string loops generically develop cusps, points where the string momentarily reaches the speed of light. These cusps produce strong gravitational wave bursts with a characteristic strain spectrum $\tilde{h}(\omega)\propto (G\mu)\,\omega^{-4/3}$, making them prime targets for gravitational wave searches, where $\mu$ is the string mass per unit length. However, this picture is modified when one accounts for gravitational backreaction. Using a convenient gauge, we reformulate the Nambu-Goto equations of motion for a loop moving in its own dynamically sourced gravitational field, enabling the first continuous numerical evolution of loops under this backreaction. A key finding is that locally cusps survive backreaction. Nevertheless, the gravitational waveform calculated in this formalism is significantly modified, weakening the cusp burst and introducing a high-frequency cutoff at $f_c\propto(G\mu)^{-3/2}$. This suppression above $f_c$ reduces the expected signal-to-noise ratio in current and near-future detectors relative to unperturbed waveform predictions.

gr-qc

Advantages of multistage quantum walks over QAOA

Methods to find the solution state for optimization problems encoded into Ising Hamiltonians are a very active area of current research. In this work we compare the quantum approximate optimization algorithm (QAOA) with multi-stage quantum walks (MSQW). Both can be used as variational quantum algorithms, where the control parameters are optimized classically. A fair comparison requires both quantum and classical resources to be assessed. Alternatively, parameters can be chosen heuristically, as we do in this work, providing a simpler setting for comparisons. Using both numerical and analytical methods, we obtain evidence that MSQW outperforms QAOA, using equivalent resources. We also show numerically for random spin glass ground state problems that MSQW performs well even for few stages and heuristic parameters, with no classical optimization.

quant-ph