Quantum Topological Data Encoding
Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high-dimensional data in Hilbert spaces, but its practical success depends critically on how classical data is encoded into quantum states. We introduce \emph{quantum topological data encoding} (QTDE), a general framework for encoding topological information into quantum states via topology-driven quantum evolution. Our method generalises an existing topology-driven quantum encoding framework to higher-dimensional data. We test the proposed method...
| Authors | Adam Wesołowski, Dimitrios Thanos, Daniel Leykam et al. |
|---|---|
| Primary category | quant-ph |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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