41 citations · 172 across the 13 of their papers we have counts for
13 papers
Lindblad engineering for quantum Gibbs state preparation under the eigenstate thermalization hypothesis
Eric Brunner, Luuk Coopmans, Gabriel Matos +3
Building upon recent progress in Lindblad engineering for quantum Gibbs state preparation algorithms, we propose a simplified protocol that is shown to be efficient under the eigen…
Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Cenk Tüysüz, Maria Demidik, Luuk Coopmans +5
In recent years, researchers have been exploring ways to generalize Boltzmann machines (BMs) to quantum systems, leading to the development of variations such as fully-visible and…
Measuring Correlation and Entanglement between Molecular Orbitals on a Trapped-Ion Quantum Computer
Gabriel Greene-Diniz, Chris N. Self, Michal Krompiec +4
Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing…
Quixer: A Quantum Transformer Model
Nikhil Khatri, Gabriel Matos, Luuk Coopmans +1
Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum…
Quantum Semidefinite Programming with Thermal Pure Quantum States
Oscar Watts, Yuta Kikuchi, Luuk Coopmans
Semidefinite programs (SDPs) are a particular class of convex optimization problems with applications in combinatorial optimization, operational research, and quantum information s…
On the Sample Complexity of Quantum Boltzmann Machine Learning
Luuk Coopmans, Marcello Benedetti
Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. We give an operational definition of QBM learning in terms of the difference in e…
Training Quantum Boltzmann Machines with the -Variational Quantum Eigensolver
Onno Huijgen, Luuk Coopmans, Peyman Najafi +2
The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy…
Realization of quantum signal processing on a noisy quantum computer
Yuta Kikuchi, Conor Mc Keever, Luuk Coopmans +2
Quantum signal processing (QSP) is a powerful toolbox for the design of quantum algorithms and can lead to asymptotically optimal computational costs. Its realization on noisy quan…
Predicting Gibbs-State Expectation Values with Pure Thermal Shadows
Luuk Coopmans, Yuta Kikuchi, Marcello Benedetti
The preparation and computation of many properties of quantum Gibbs states is essential for algorithms such as quantum semidefinite programming and quantum Boltzmann machines. We p…
Data-Driven Time Propagation of Quantum Systems with Neural Networks
James Nelson, Luuk Coopmans, Graham Kells +1
We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data…
Optimal Control in Disordered Quantum Systems
Luuk Coopmans, Steve Campbell, Gabriele De Chiara +1
We investigate several control strategies for the transport of an excitation along a spin chain. We demonstrate that fast, high fidelity transport can be achieved using protocols d…
Dynamical Phase Error in Interacting Topological Quantum Memories
Luuk Coopmans, Shane Dooley, Ian Jubb +2
A local Hamiltonian with Topological Quantum Order (TQO) has a robust ground state degeneracy that makes it an excellent quantum memory candidate. This memory can be corrupted howe…
Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
Luuk Coopmans, Di Luo, Graham Kells +2
Quantum control, which refers to the active manipulation of physical systems described by the laws of quantum mechanics, constitutes an essential ingredient for the development of…