AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Sampling from discrete Markov random fields (MRFs) is a hard problem and amplitude-encoded i.i.d. sampling for small MRFs where $2^n$ target probabilities are precomputed classically is studied to allow a clean comparison against classical MCMC based on independent circuit samples.
The first adaptation of Pauli Correlation Encoding to quantum topological data analysis is presented, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register, reformulating Betti number estimation as a depth-efficient variational optimization over a compresse...
A protocol for approximating the measurement distributions of quantum states, extending beyond standard observable estimation is introduced, and tightened gate complexity bounds for practically relevant systems, including those with k-local interactions, long-tailed matrix ensembles, and conserved quantities are provid...
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