Quantum

Prototype-Guided Optimization of Quantum Data Embeddings with Linear Cost

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Briefings

A new method reduces quantum data embedding optimization costs from quadratic to linear complexity by using prototype-guided objectives. The framework integrates quantum circuit architecture optimization with continuous parameter refinement, achieving competitive SVM performance on five benchmark datasets with up to 20 qubits while significantly lowering fidelity evaluation overhead compared to pairwise optimization.

Key points

• The method reduces fidelity-evaluation cost from O(B^2) to O(B) by using prototype-based objectives instead of pairwise evaluations.
• The framework combines task-specific quantum circuit architecture optimization with continuous refinement of data-embedding parameters.

Context

This research addresses a bottleneck in supervised quantum machine learning where existing data-adaptive approaches rely on pairwise fidelity evaluations that scale quadratically with sample size, creating a computational bottleneck for iterative embedding optimization.

Limitations

Results are limited to five benchmark datasets with up to 20 qubits; geometric analyses reveal dataset-dependent benefits and limitations not generalizable to all quantum data structures.

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