23 citations · 23 across the 1 of their papers we have counts for
5 papers
Enhancing quantum models of stochastic processes with error mitigation
Matthew Ho, Ryuji Takagi, Mile Gu
Error mitigation has been one of the recently sought after methods to reduce the effects of noise when computation is performed on a noisy near-term quantum computer. Interest in s…
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
Matthew Ho, Arya Farahi, Markus Michael Rau +1
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical bac…
Aging Halos: Implications of the Magnitude Gap on Conditional Statistics of Stellar and Gas Properties of Massive Halos
Arya Farahi, Matthew Ho, Hy Trac
Cold dark matter model predicts that the large-scale structure grows hierarchically. Small dark matter halos form first. Then, they grow gradually via continuous merger and accreti…
Robust inference of memory structure for efficient quantum modelling of stochastic processes
Matthew Ho, Mile Gu, Thomas J. Elliott
A growing body of work has established the modelling of stochastic processes as a promising area of application for quantum techologies; it has been shown that quantum models are a…
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Matthew Ho, Markus Michael Rau, Michelle Ntampaka +3
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters wi…