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Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
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We formalize a rigorous connection between barren plateaus (BP) in variational quantum algorithms and exponential concentration of quantum kernels for machine learning. Our results imply that recently proposed strategies to build BP-free quantum circuits can be utilized to construct useful quantum kernels for machine learning. This is illustrated by a numerical example employing a provably BP-free quantum neural network to construct kernel matrices for classification datasets of increasing dimensionality without exponential concentration.
Forward citations
Cited by 2 Pith papers
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Concentration-Free Quantum Kernel Learning in the Rydberg Blockade
A Rydberg blockade based quantum kernel is claimed to avoid exponential concentration while remaining classically hard to simulate.
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Qutrit-Based Neural Quantum Kernels for Classification Tasks
Qutrit neural quantum kernels beat matched QNN baselines on four benchmarks, with gains that depend on feature budget, register size, and SU(3) parameterization, under noiseless simulation.
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