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Quantum neural network

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arxiv quant-ph/0107012 v2 pith:Z52V5R7O submitted 2001-07-03 quant-ph

Quantum neural network

classification quant-ph
keywords networkneuralquantumimplementedopticalartificialbeambuilt
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It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with different polarization, the weights of the QNN can be implemented by optical beam splitters and phase shifters

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Forward citations

Cited by 2 Pith papers

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  1. Parameter-Shift Rules for Gradients in Boson Sampling Experiments

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    Lossy Fock boson sampling probabilities are finite Fourier series in each phase, so their exact gradients can be recovered from shifted photon-count measurements; Gaussian boson sampling under general loss admits no s...

  2. Research progress on quantum neural networks and quantum machine learning

    quant-ph 2026-05 unverdicted novelty 2.0

    Survey summarizing performance metrics of fully connected QNNs, quantum CNNs, equivariant QNNs, quantum Hopfield networks, quantum Boltzmann machines, quantum reservoir computing, and composite networks for reinforcem...