Quantum tomography using state-preparation unitaries
Joran van Apeldoorn, Arjan Cornelissen, et al.
SODA 2023
Motivated by the problem of fitting a surrogate model to a set of feasible points in the context of constrained derivative-free optimization, we consider the problem of selecting a small set of points with good space-filling and orthogonality properties from a larger set of feasible points. We propose four mixed-integer linear programming models for this task and we show that the corresponding optimization problems are NP-hard. Numerical experiments show that our models consistently yield well-distributed points that, on average, help reducing the variance of model fitting errors.
Joran van Apeldoorn, Arjan Cornelissen, et al.
SODA 2023
Enrico Malaguti, Giacomo Nannicini, et al.
Electron. Notes Discrete Math.
Giacomo Nannicini
SIAM Review
Giacomo Nannicini, Lev S. Bishop, et al.
ACM TQC