Hartmut Neven

Totally Corrective Boosting with Cardinality Penalization

2015 · arXiv (Cornell University)

We propose a totally corrective boosting algorithm with explicit cardinality regularization. The resulting combinatorial optimization problems are not known to be efficiently solvable with existing classical methods, but emerging quantum optimization technology gives hope for …

Tunable inductive coupling of superconducting qubits in the strongly nonlinear regime

2017 · Physical Review A

For a variety of superconducting qubits, tunable interactions are achieved through mutual inductive coupling to a coupler circuit containing a nonlinear Josephson element. In this paper, we derive the general interaction mediated by such a …

The Snake Optimizer for Learning Quantum Processor Control Parameters

2020 · arXiv (Cornell University)

High performance quantum computing requires a calibration system that learns optimal control parameters much faster than system drift. In some cases, the learning procedure requires solving complex optimization problems that are non-convex, high-dimensional, highly constrained, …

Machine learning of high dimensional data on a noisy quantum processor

2021 · npj Quantum Information

Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully …