Parity Supervision as a Driver of Generalization in Quantum Generative Modeling

Abstract:Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid but previously unseen states. In a controlled benchmark, we test whether parity-based training provides an inductive bias for this kind of generalization in instantaneous quantum polynomial-time (IQP) circuit Born machines. We compare the same IQP circuit trained with parity supervision and coordinate-wise mean-squared error (MSE), together with classical controls. Parity supervision improves exact distributional fit and recovery of unseen high-value states over IQP-MSE. A circuit-free spectral reconstruction shows that the matched parity moments already transfer evidence from observed samples to structurally compatible unseen states, while the IQP circuit further refines this structure. These results identify parity supervision as both a tractable training signal and a generalization mechanism when the target distribution, training objective, and circuit architecture are spectrally aligned.

11 pages, 7 figures. Accepted at the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE 2026), QGDD Technical Papers track

arXiv:2605.10258 [quant-ph]

(or arXiv:2605.10258v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2605.10258 Focus to learn more arXiv-issued DOI via DataCite

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