The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
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Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
citing papers explorer
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Measurement-based quantum machine learning
The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
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Neural posterior estimation of Galactic Binary signals for the LISA mission
Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
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Divide et impera: hybrid multinomial classifiers from quantum binary models
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.