28 citations · 65 across the 5 of their papers we have counts for
4 papers · 1 filter
Regularizing quantum loss landscapes by noise injection
Daniil S. Bagaev, Maxim A. Gavreev, Alena S. Mastiukova +2
The difficulty of training variational quantum algorithms and quantum machine learning models is well established. In particular, quantum loss landscapes are often highly non-conve…
Barren plateaus are swamped with traps
Nikita A. Nemkov, Evgeniy O. Kiktenko, Aleksey K. Fedorov
Two main challenges preventing efficient training of variational quantum algorithms and quantum machine learning models are local minima and barren plateaus. Typically, barren plat…
Demonstration of a parity-time symmetry breaking phase transition using superconducting and trapped-ion qutrits
Alena S. Kazmina, Ilia V. Zalivako, Alexander S. Borisenko +21
Scalable quantum computers hold the promise to solve hard computational problems, such as prime factorization, combinatorial optimization, simulation of many-body physics, and quan…
Fourier expansion in variational quantum algorithms
Nikita A. Nemkov, Evgeniy O. Kiktenko, Aleksey K. Fedorov
The Fourier expansion of the loss function in variational quantum algorithms (VQA) contains a wealth of information, yet is generally hard to access. We focus on the class of varia…