Without reference information, supervised quantum classifiers are forced to output the same label for every state orthogonal to the training span.
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3 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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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.
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.
citing papers explorer
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No Reference-Free Generalization in Quantum Machine Learning
Without reference information, supervised quantum classifiers are forced to output the same label for every state orthogonal to the training span.
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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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The power and limitations of learning quantum dynamics incoherently
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.