Search Results for author:
Found 4 papers, 1 papers with code
Stochastic noise can be helpful for variational quantum algorithms
Saddle points constitute a crucial challenge for first-order gradient descent algorithms.
Scalably learning quantum many-body Hamiltonians from dynamical data
The physics of a closed quantum mechanical system is governed by its Hamiltonian.
Single-component gradient rules for variational quantum algorithms
A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations.
Stochastic gradient descent for hybrid quantum-classical optimization
We formalize this notion, which allows us to show that in many relevant cases, including VQE, QAOA and certain quantum classifiers, estimating expectation values with $k$ measurement outcomes results in optimization algorithms whose convergence properties can be rigorously well understood, for any value of $k$.
