Outputs (57)

Simulating quantum field theories on continuous-variable quantum computers (2024)
Journal Article
Abel, S., Spannowsky, M., & Williams, S. (2024). Simulating quantum field theories on continuous-variable quantum computers. Physical Review A, 110(1), Article 012607. https://doi.org/10.1103/physreva.110.012607

We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of continuous-variable quantum computing (CVQC) to repro... Read More about Simulating quantum field theories on continuous-variable quantum computers.

Training neural networks with universal adiabatic quantum computing (2024)
Journal Article
Abel, S., Criado, J. C., & Spannowsky, M. (2024). Training neural networks with universal adiabatic quantum computing. Frontiers in Artificial Intelligence, 7, Article 1368569. https://doi.org/10.3389/frai.2024.1368569

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum computing (AQC), a paradigm that leverages the principle... Read More about Training neural networks with universal adiabatic quantum computing.

Decoding Nature with Nature's Tools: Heterotic Line Bundle Models of Particle Physics with Genetic Algorithms and Quantum Annealing (2023)
Journal Article
Abel, S. A., Constantin, A., Harvey, T. R., Lukas, A., & Nutricati, L. A. (2024). Decoding Nature with Nature's Tools: Heterotic Line Bundle Models of Particle Physics with Genetic Algorithms and Quantum Annealing. Fortschritte der Physik, 72(2), Article 2300260. https://doi.org/10.1002/prop.202300260

The string theory landscape may include a multitude of ultraviolet embeddings of the Standard Model, but identifying these has proven difficult due to the enormous number of available string compactifications. Genetic Algorithms (GAs) represent a pow... Read More about Decoding Nature with Nature's Tools: Heterotic Line Bundle Models of Particle Physics with Genetic Algorithms and Quantum Annealing.

String Model Building on Quantum Annealers (2023)
Journal Article
Abel, S., Nutricati, L. A., & Rizos, J. (2023). String Model Building on Quantum Annealers. Fortschritte der Physik, 71(12), Article 2300167. https://doi.org/10.1002/prop.202300167

For the first time the direct construction of string models on quantum annealers has been explored and has been investigated their efficiency and effectiveness in the model discovery process. Through a thorough comparison with traditional methods suc... Read More about String Model Building on Quantum Annealers.

Running of gauge couplings in string theory (2023)
Journal Article
Abel, S., Dienes, K. R., & Nutricati, L. A. (2023). Running of gauge couplings in string theory. Physical Review D, 107(12), Article 126019. https://doi.org/10.1103/physrevd.107.126019

In this paper we conduct a general, model-independent analysis of the running of gauge couplings within closed string theories. Unlike previous discussions in the literature, our calculations fully respect the underlying modular invariance of the str... Read More about Running of gauge couplings in string theory.

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.