QNNs retain most hidden-task signals through public-task interfaces while classical networks transmit little, with transmission governed by teacher drift magnitude and the visible fraction of hidden drift in a unified geometric model.
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4 Pith papers cite this work. Polarity classification is still indexing.
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QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
Variational quantum circuits learn entanglement orbits under local unitary actions to classify four-qubit stabilizer states.
citing papers explorer
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Quantum Subliminal Learning
QNNs retain most hidden-task signals through public-task interfaces while classical networks transmit little, with transmission governed by teacher drift magnitude and the visible fraction of hidden drift in a unified geometric model.
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Experimentally validated quantum-secure federated learning over a multi-user quantum network
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
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Quantum encodings that preserve persistent homology
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
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A hybrid variational quantum circuit approach for stabilizer states classifiers
Variational quantum circuits learn entanglement orbits under local unitary actions to classify four-qubit stabilizer states.