Trainability and Mode Separation of Mixed IQP-QCBMs

Abstract:Quantum circuit Born machines (QCBMs) based on instantaneous quantum polynomial-time (IQP) circuits are promising quantum generative models for their classical trainability. It is known that their ancilla-free form avoids barren plateaus under certain initializations, but remains non-universal. Although adding ancilla qubits raises the expressivity, whether the ancilla-extended model retains local trainability remains unknown. We propose the mixed IQP-QCBM, which generalizes the ancilla-extended circuit as a weighted mixture of ancilla-free IQP circuits, called branches. For a polynomial number of branches, we prove local barren-plateau avoidance from data-agnostic and, under certain assumptions, data-dependent initializations. We further show that the mixed IQP-QCBM can surpass the best ancilla-free IQP circuit only if its branches generate a number of distinct distributions. In particular, we focus on a behavior we call \emph{mode separation}, in which each branch captures a particular feature of the target. Mode separation is hard to attain from an initialization whose branches generate the same distribution: the gradients that would separate them are suppressed while the distributions they generate remain close. This motivates \emph{cluster initialization}, which assigns a different unsupervised data cluster to each branch and provides an initial degree of mode separation. Exact calculations on two 16-bit datasets support the barren-plateau and gradient-suppression claims. On four benchmarks, binary clusters, a two-dimensional Ising model, binarized MNIST, and a 484-spin glass, cluster initialization converges fastest and reaches the lowest mean test $\mathrm{MMD}^2$. We observe that, when achieving the lowest test $\mathrm{MMD}^2$, the mixed IQP-QCBM contains branches specialized to distinguishable data features such as blob patterns, magnetization sectors, or digit shapes.

arXiv:2607.27883 [quant-ph]

(or arXiv:2607.27883v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.27883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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