338 citations · 2.1k across the 62 of their papers we have counts for
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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…
Shallow shadows: Expectation estimation using low-depth random Clifford circuits
Christian Bertoni, Jonas Haferkamp, Marcel Hinsche +3
We provide practical and powerful schemes for learning many properties of an unknown n-qubit quantum state using a sparing number of copies of the state. Specifically, we present a…
Time-energy uncertainty relation for noisy quantum metrology
Philippe Faist, Mischa P. Woods, Victor V. Albert +3
Detection of weak forces and precise measurement of time are two of the many applications of quantum metrology to science and technology. We consider a quantum system initialized i…
Computational advantage of quantum random sampling
Dominik Hangleiter, Jens Eisert
Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementatio…