• Abstract Quantum computing offers significant potential for tackling complex problems, yet preparing quantum states from real-world data remains a critical challenge. • We introduce the statistics-informed parameterized quantum circuit (SI-PQC), an approach specifically designed to efficiently prepare arbitrary statistical distributions. • By leveraging statistical symmetries in data through the maximum entropy principle, SI-PQC encodes prior information with a fixed-structure circuit and tunable parameters, eliminating extensive pre-processing. • This method achieves exponential resource savings in preparing mixture models, crucial for applications in statistics and machine learning. • SI-PQC also supports variational learning within an optimally dimensioned training space, enhancing generalization, trainability and statistical interpretability. • Numerical experiments confirm that SI-PQC can effectively prepare diverse distributions and accurately learn Gaussian mixture models, aligning closely with theoretical expectations.
Article Summaries:
- Researchers have introduced a “statistics‑informed parameterized quantum circuit” (SI‑PQC) that streamlines the preparation of arbitrary statistical distributions on quantum hardware. By embedding prior knowledge through the maximum‑entropy principle, the circuit uses a fixed topology with tunable parameters, eliminating extensive pre‑processing and achieving exponential savings when encoding mixture models. SI‑PQC also supports variational learning within an optimally sized training space, improving generalization, trainability, and interpretability. Numerical tests confirm accurate preparation of diverse distributions and learning of Gaussian mixtures. The authors highlight practical gains in financial derivative pricing and real‑time risk analysis, positioning SI‑PQC as a versatile subroutine for data‑driven quantum applications in finance, machine learning, and diagnostics.
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