5 papers
Perturbative Contrastive Physical Learning
Kyungeun Kim, Amanuel Anteneh, Israel Klich +2
Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slightly different conditions pr…
Laser interferometry as a robust neuromorphic platform for machine learning
Amanuel Anteneh, Kyungeun Kim, J. M. Schwarz +2
We present a method for implementing an optical neural network using only linear optical resources, namely field displacement and interferometry applied to coherent states of light…
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
Amanuel Anteneh
We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty…
Gradient-descent methods for scalable quantum detector tomography
Amanuel Anteneh, Olivier Pfister
We present a technique for performing quantum detector tomography (QDT) of phase insensitive quantum detectors, a category under which many detectors of interest fall under, using…
Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
Amanuel Anteneh, Léandre Brunel, Carlos González-Arciniegas +1
Cubic-phase states are a sufficient resource for universal quantum computing over continuous variables. We present results from numerical experiments in which deep neural networks…