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Toward Trainability of Quantum Neural Networks
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Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform classical models, serious bottlenecks exist for training QNNs; namely, QNNs with random structures have poor trainability due to the vanishing gradient with rate exponential to the input qubit number. The vanishing gradient could seriously influence the applications of large-size QNNs. In this work, we provide a viable solution with theoretical guarantees. Specifically, we prove that QNNs with tree tensor and step controlled architectures have gradients that vanish at most polynomially with the qubit number. We numerically demonstrate QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
Forward citations
Cited by 4 Pith papers
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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.
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A sample-to-centroid fidelity kernel enables linear-scaling multiclass quantum classification; in simulation it beats pure quantum baselines, and untrained 124-qubit hardware results match an RBF kernel.
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Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Classical surrogates using truncated trigonometric expansions emulate noisy quantum processors and cut measurement overhead in VQE pre-training and Floquet phase identification.
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