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One-Clean-Qubit Quantum Estimation of Markov Random Field Partition Functions

A quantum-computing approach to estimating the partition function of a Markov random field, the exponentially expensive normalizing constant needed to turn its potentials into a probability distribution. The method casts the estimation as a trace-like quantity computable in the one-clean-qubit (DQC1) model, aiming to exploit quantum scalability where classical sampling, variational, and belief-propagation methods become intractable, and is demonstrated on an airborne-radar anomaly-localization problem.