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Automated tuning of double quantum dots into specific charge states using neural networks
TLDR
We introduce an algorithm driven by machine learning that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state. Expand
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Unsupervised identification of topological order using predictive models
Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matterExpand
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Correlation functions and conditioned quantum dynamics in photodetection theory
Correlations in photodetection signals from quantum light sources are conventionally calculated by application of the source master equation and the quantum regression theorem. In this article weExpand
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Hamiltonian Learning for Quantum Error Correction
TLDR
We introduce a machine-learning driven method for scalable quantum Hamiltonian learning based on a toy model relevant for quantum information processing, the toric code. Expand
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Quantum parameter estimation with a neural network
We propose to use neural networks to estimate the rates of coherent and incoherent processes in quantum systems from continuous measurement records. In particular, we adapt an image recognitionExpand
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Degradability of Fermionic Gaussian Channels.
We study the degradability of fermionic Gaussian channels. Fermionic quantum channels are a central building block of quantum information processing with fermions, and the family of GaussianExpand
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Fully automated identification of 2D material samples.
Thin nanomaterials are key constituents of modern quantum technologies and materials research. Identifying specimens of these materials with properties required for the development of state of theExpand
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Let's take this discussion online
With the lockdowns caused by the COVID-19 pandemic, researchers turn to online conferencing. While posing new challenges, this format also brings multiple advantages. We argue that virtualExpand
Introduction to Machine Learning for the Sciences
TLDR
This lecture is an introduction to basic machine learning algorithms for scientists. Expand
Solving optimization tasks in condensed matter