213 citations · 265 across the 5 of their papers we have counts for
6 papers · 1 filter
Learning topological states from randomized measurements using variational tensor network tomography
Yanting Teng, Rhine Samajdar, Katherine Van Kirk +5
Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic meth…
An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory
Niklas Pirnay, Vincent Ulitzsch, Frederik Wilde +2
Combinatorial optimization - a field of research addressing problems that feature strongly in a wealth of scientific and industrial contexts - has been identified as one of the cor…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…
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…
Single-component gradient rules for variational quantum algorithms
Thomas Hubregtsen, Frederik Wilde, Shozab Qasim +1
Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical set…
Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Meyer +4
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…