79 citations · 514 across the 32 of their papers we have counts for
8 papers · 1 filter
Toward hybrid quantum simulations with qubits and qumodes on trapped-ion platforms
Jack Y. Araz, Matt Grau, Jake Montgomery +1
We explore the feasibility of gate-based hybrid quantum computing using both discrete (qubit) and continuous (qumode) variables on trapped-ion platforms. Trapped-ion systems have d…
The role of data embedding in quantum autoencoders for improved anomaly detection
Jack Y. Araz, Michael Spannowsky
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects…
State preparation of lattice field theories using quantum optimal control
Jack Y. Araz, Siddhanth Bhowmick, Matt Grau +2
We explore the application of quantum optimal control (QOC) techniques to state preparation of lattice field theories on quantum computers. As a first example, we focus on the Schw…
Thermal state preparation of the SYK model using a variational quantum algorithm
Jack Y. Araz, Raghav G. Jha, Felix Ringer +1
We study the preparation of thermal states of the dense and sparse Sachdev-Ye-Kitaev (SYK) model using a variational quantum algorithm for Majorana fermions over a…
Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
Jack Y. Araz, Michael Spannowsky, Matthew Wingate
This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle…
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…
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…