30 citations · 65 across the 4 of their papers we have counts for
4 papers
A super-polynomial quantum-classical separation for density modelling
Niklas Pirnay, Ryan Sweke, Jens Eisert +1
Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, w…
Scalably learning quantum many-body Hamiltonians from dynamical data
Frederik Wilde, Augustine Kshetrimayum, Ingo Roth +3
The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all…
A single -gate makes distribution learning hard
Marcel Hinsche, Marios Ioannou, Alexander Nietner +6
The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly inter…
Transparent reporting of research-related greenhouse gas emissions through the scientific COnduct initiative
Ryan Sweke, Paul Boes, Nelly H. Y. Ng +3
Estimating the greenhouse gas emissions of research-related activities is a critical first step towards the design of mitigation policies and actions. Here we propose and motivate…