5 citations · 6 across the 3 of their papers we have counts for
6 papers
Scalable quantum processor noise characterization
Kathleen E. Hamilton, Tyler Kharazi, Titus Morris +3
Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can…
Error-mitigated data-driven circuit learning on noisy quantum hardware
Kathleen E. Hamilton, Raphael C. Pooser
Application-inspired benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the pre…
Spike-based primitives for graph algorithms
Kathleen E. Hamilton, Tiffany M. Mintz, Catherine D. Schuman
In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking ne…
Generative model benchmarks for superconducting qubits
Kathleen E. Hamilton, Eugene F. Dumitrescu, Raphael C. Pooser
In this work we experimentally demonstrate how generative model training can be used as a benchmark for small ( qubits) quantum devices. Performance is quantified using three d…
Community detection with spiking neural networks for neuromorphic hardware
Kathleen E. Hamilton, Neena Imam, Travis S. Humble
We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph G to a sy…
Spectral bounds for percolation on directed and undirected graphs
Kathleen E. Hamilton, Leonid P. Pryadko
We give several algebraic bounds for percolation on directed and undirected graphs: proliferation of strongly-connected clusters, proliferation of in- and out-clusters, and the tra…