arXiv:2605.30866v1 Announce Type: cross Abstract: Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are embedded into quantum states. Yet the use of a fixed embedding circuit ansatz remains standard practice. We propose an energy-based generative learning framework that synthesizes gate sequences to optimize embedding structures and refine data-tailored parameters, using a fidelity-based surrogate objective to guide the search toward improved class distinguishability. Empirically, the method improve
The rapid advancement in quantum computing hardware necessitates improved methods for integrating classical data, making the development of optimized quantum data embeddings a critical next step.
This research provides a foundational improvement for quantum machine learning on classical data, potentially unlocking new performance benchmarks and wider applicability for nascent quantum algorithms.
Current fixed embedding circuit practices will evolve towards dynamic, data-tailored embedding strategies, improving the efficacy of quantum algorithms for real-world problems.
- · Quantum Machine Learning Researchers
- · Quantum Computing Hardware Manufacturers
- · Organizations developing hybrid quantum-classical applications
- · Fixed-embedding circuit methodologies
- · Classical machine learning approaches in specialized niches
Improved performance of quantum machine learning models on classical datasets.
Accelerated development and adoption of quantum algorithms across various industries due to enhanced practical utility.
A potential 'quantum advantage' in certain data analysis tasks, challenging purely classical approaches.
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Read at arXiv cs.LG