Structural f-divergence yields tight trade-off inequalities bounding gradient magnitudes and cost moments in parameterized quantum circuits, with equality for a minimal one-qubit ansatz.
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A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
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Structural $f$-divergence: Tight universal bounds for cost function moments and gradients in parameterized quantum circuits
Structural f-divergence yields tight trade-off inequalities bounding gradient magnitudes and cost moments in parameterized quantum circuits, with equality for a minimal one-qubit ansatz.
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Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.