Supervised Learning with Quantum Hamiltonian Embeddings
Machine learning is widely regarded as a promising application in which quantum computing could offer a computational advantage. However, general quantum machine learning models with provable guarantees on performance, trainability, and efficient inference remain limited. In this work, we introduce quantum Hamiltonian embeddings, a supervised learning framework in which a classical training dataset is encoded into a single effective, parameterized Hamiltonian. Training optimizes the parameters of this data-dependent Hamiltonian, whose approximate ground state defines the classification rule. Once the ground state has been approximated using a quantum algorithm, inference can be performed ...
| Authors | Riccardo Molteni, Minjun Jeon, Paolo Braccia et al. |
|---|---|
| Primary category | quant-ph |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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