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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Photon addition and subtraction enhance Gaussian-source heralded generation of dual-rail Bell, GHZ, and W states with improved fidelity and success probability.
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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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Heralded entangled state generation enhanced by photon addition and subtraction
Photon addition and subtraction enhance Gaussian-source heralded generation of dual-rail Bell, GHZ, and W states with improved fidelity and success probability.