LCQNN combines several trainable unitaries through a learned superposition on control qubits, yielding gradient variance bounds that scale polynomially with local system size rather than exponentially with total qubit count.
This proposition implies that LCQNN’s structure can be tailored to specific problems to guarantee trainability while retaining non-trivial quantum features
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LCQNN: Linear Combination of Quantum Neural Networks
LCQNN combines several trainable unitaries through a learned superposition on control qubits, yielding gradient variance bounds that scale polynomially with local system size rather than exponentially with total qubit count.