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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Constructive protocols prove that arbitrary fermionic quantum operations are achievable with global controls in optical lattice Fermi-Hubbard systems.
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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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Programmable Fermionic Quantum Processors with Globally Controlled Lattices
Constructive protocols prove that arbitrary fermionic quantum operations are achievable with global controls in optical lattice Fermi-Hubbard systems.