782 citations · 11.8k across the 249 of their papers we have counts for
6 papers · 2 filters
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
Jack Y. Araz, Michael Spannowsky
The Hamiltonian of an isolated quantum mechanical system determines its dynamics and physical behaviour. This study investigates the possibility of learning and utilising a system'…
Towards a Quantum Simulation of Nonlinear Sigma Models with a Topological Term
Jack Y. Araz, Sebastian Schenk, Michael Spannowsky
We determine the mass gap of a two-dimensional nonlinear sigma model augmented with a topological -term using tensor network and digital quantum algorithms. As proof of p…
A Genetic Quantum Annealing Algorithm
Steven Abel, Luca A. Nutricati, Michael Spannowsky
A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA…
Qade: Solving Differential Equations on Quantum Annealers
Juan Carlos Criado, Michael Spannowsky
We present a general method, called Qade, for solving differential equations using a quantum annealer. The solution is obtained as a linear combination of a set of basis functions.…
Completely Quantum Neural Networks
Steve Abel, Juan C. Criado, Michael Spannowsky
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 introducin…
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Jack Y. Araz, Michael Spannowsky
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensi…