4 citations · 7 across the 7 of their papers we have counts for
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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…
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…
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…
Machine learning for efficient generation of universal hybrid quantum computing resources
Amanuel Anteneh, Olivier Pfister
We present numerical simulations of deep reinforcement learning on a measurement-based quantum processor--a time-multiplexed optical circuit sampled by photon-number-resolving dete…
Sample efficient graph classification using binary Gaussian boson sampling
Amanuel Anteneh, Olivier Pfister
We present a variation of a quantum algorithm for the machine learning task of classification with graph-structured data. The algorithm implements a feature extraction strategy tha…