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About this paper
Chen-Yu Liu, Leonardo Placidi, Eric Brunner, Enrico Rinaldi
A diagnostic framework, the Correlation-Complexity Map, guides the selection of classical datasets for quantum generative modeling using IQP circuits, enhanced by a float-to-bitstring representation and a latent-parameter adaptation scheme. This approach enabled an IQP model to generate turbulence data with competitive fidelity using fewer parameters and less training data than classical baselines.
Key insights
- Optimization of IQP circuits to maximize QCLI concurrently leads to an emergent increase in CCI, indicating a natural coupling between IQP-compatible interference patterns and complex multivariate classical correlations.
- The Quantum Correlation–Likeness Indicator (QCLI) serves as a predictor for the achievable Maximum Mean Discrepancy (MMD) loss, suggesting better data-architecture alignment leads to lower approximation errors.
- The Correlation–Complexity Map identified turbulence data as a prime candidate for IQP generative models due to its simultaneous high QCLI (strong IQP-like interference) and high CCI (complex non-pairwise dependencies).
Summary by alphaXiv.
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