2 papers
quant-ph2025
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…
quant-ph2025
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…