Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
Approxi- mate unitary 饾憽-designs by short random quantum circuits using nearest-neighbor and long-range gates
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Wavelet scaling 伪 = 1/2 separates classically simulable area-law from volume-law phases for quantum kernels in world-model latents, with empirical VideoMAE latents and a 螛(d^{-2}) variance bound implying simulation hardness and quadratic measurement costs.
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Quantum computation at the edge of chaos
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
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Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Wavelet scaling 伪 = 1/2 separates classically simulable area-law from volume-law phases for quantum kernels in world-model latents, with empirical VideoMAE latents and a 螛(d^{-2}) variance bound implying simulation hardness and quadratic measurement costs.