All Outputs (5)

Cosmic Inflation and Genetic Algorithms (2022)
Journal Article
Abel, S. A., Constantin, A., Harvey, T. R., & Lukas, A. (2023). Cosmic Inflation and Genetic Algorithms. Fortschritte der Physik, 71(1), https://doi.org/10.1002/prop.202200161

Large classes of standard single-field slow-roll inflationary models consistentwith the required number of e-folds, the current bounds on the spectral indexof scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation canbe efficien... Read More about Cosmic Inflation and Genetic Algorithms.

Quantum optimization of complex systems with a quantum annealer (2022)
Journal Article
Abel, S., Blance, A., & Spannowsky, M. (2022). Quantum optimization of complex systems with a quantum annealer. Physical Review A, 106(4), https://doi.org/10.1103/physreva.106.042607

We perform an in-depth comparison of quantum annealing with several classical optimization techniques, namely, thermal annealing, Nelder-Mead, and gradient descent. The focus of our study is large quasicontinuous potentials that must be encoded using... Read More about Quantum optimization of complex systems with a quantum annealer.

Ising Machines for Diophantine Problems in Physics (2022)
Journal Article
Abel, S. A., & Nutricati, L. A. (2022). Ising Machines for Diophantine Problems in Physics. Fortschritte der Physik, 70(11), Article 2200114. https://doi.org/10.1002/prop.202200114

Diophantine problems arise frequently in physics, in for example anomaly cancellation conditions, string consistency conditions and so forth. We present methods to solve such problems to high order on annealers that are based on the quadratic Ising M... Read More about Ising Machines for Diophantine Problems in Physics.

Completely quantum neural networks (2022)
Journal Article
Abel, S., Criado, J. C., & Spannowsky, M. (2022). Completely quantum neural networks. Physical Review A, 106(2), Article 022601. https://doi.org/10.1103/physreva.106.022601

Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a sta... Read More about Completely quantum neural networks.

Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning (2022)
Journal Article
Abel, S., Constantin, A., Harvey, T. R., & Lukas, A. (2022). Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning. Fortschritte der Physik, 70(5), Article 2200034. https://doi.org/10.1002/prop.202200034

The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and searc... Read More about Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning.