An End-to-End Hybrid Quantum-Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study
Read the original on arXiv Machine Learning →The paper presents an end‑to‑end hybrid quantum‑classical workflow for sampling from discrete Markov random fields (MRFs). By precomputing the $2^n$ target probabilities classically, the authors compare amplitude‑encoded quantum sampling against several modern classical MCMC methods across 60 instances from five graph families, finding that classical samplers achieve mean effective sample size (ESS) ratios close to the quantum approach. Wall‑clock benchmarks show that exact inverse‑CDF sampling outperforms the quantum sampler by a factor of 36 on average, and that matrix product state (MPS) techniques yield far higher fidelities than variational quantum circuits (VQC) at comparable compression budgets.
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