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Motivated by applications of quantum computers in Gibbs sampling fromcontinuous real-valued functions, we ask whether such algorithms can providepractical advantages for machine learning models trained on classical data andseek measures for quantifying such impacts. In this study, we focus on deepenergy-based models (EBM), as they require continuous-domain Gibbs samplingboth during training and inference. In lieu of fault-tolerant quantum computersthat can execute quantum Gibbs sampling algorithms, we use the Monte Carlosimulation of diffusion processes as a classical alternative. Morespecifically, we investigate whether long-run persistent chain Monte Carlosimulation of Langevin dynamics improves the quality of the representationsachieved by EBMs. We consider a scheme in which the Monte Carlo simulation of adiffusion, whose drift is given by the gradient of the energy function, is usedto improve the adversarial robustness and calibration score of an independentclassifier network. Our results show that increasing the computational budgetof Gibbs sampling in persistent contrastive divergence improves both thecalibration and adversarial robustness of the model, suggesting a prospectiveavenue of quantum advantage for generative AI using future large-scale quantumcomputers.

Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI