Speaker
Description
Instantaneous quantum polynomial-time (IQP) circuits provide a natural framework for quantum generative modeling and are believed to be classically hard to sample from in certain regimes. We study the optimization landscape of IQP-based quantum circuit Born machines trained with Maximum Mean Discrepancy (MMD) losses constructed from low-body Pauli-$Z$ correlators. We prove that random full-range initializations exhibit barren plateaus, while identity and unbiased data-agnostic initializations admit non-vanishing loss variance lower bounds. We further introduce a data-dependent initialization that better aligns with the target distribution, yielding provable gradients and faster convergence in practice, including experiments on 150-qubit genomic datasets.
| Topic | Quantum variational circuits and their applications |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |