AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results show that forward-only access cannot provide an asymptotic query-complexity advantage over learning from classical data or quantum data copies in this worst-case setting, and establish the essential role of inverse access in the known realizable-setting improvement.
The results show that the factorization underlying a quantum block encoding can itself provide sufficient classical structure even when sampling-and-query access to the composite matrix is unavailable, suggesting a classical sampler with prescribed accuracy and polynomially related runtime.
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