Parity Supervision as a Driver of Generalization in Quantum Generative Modeling

Abstract: Generalizing from finite samples to unseen valid states is central to discrete generative modeling. In a controlled, exactly enumerable setting, we test whether parity losses, commonly used for tractable Instantaneous Quantum Polynomial-time (IQP) training, also provide an inductive bias for generalization. We compare an IQP circuit Born machine trained by parity supervision with the same circuit trained by coordinate-wise mean-squared-error (MSE), and with a classical maximum-entropy control given the same parity moments. Parity supervision improves exact forward Kullback-Leibler (KL) fit and unseen high-value-state recovery over IQP-MSE, while the maximum-entropy control does not reproduce the full effect. A parameter-free spectral reconstruction shows that parity moments already transfer evidence from observed samples to structurally compatible unseen states, which the IQP circuit further refines. This identifies parity supervision not only as a tractable training signal, but also as a generalization mechanism for IQP Born machines when the distribution to be learned, the parity objective, and the circuit architecture are structurally aligned.