Supervised Learning with Quantum Hamiltonian Embeddings
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.
ProofPaper ↗
Key points
- 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.
- Once the ground state has been approximated using a quantum algorithm, inference can be performed either classically using a classical representation of the learned state, or with a quantum representation of the state.
- Furthermore, under standard cryptographic assumptions, we show that Hamiltonian embeddings can realize hypothesis functions beyond the reach of efficient classical algorithms.
Sources (1)
- [1]Supervised Learning with Quantum Hamiltonian EmbeddingsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:56 PM
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.
Machine learning is widely regarded as a promising application in which quantum computing could offer a computational advantage.
Extractive summary: sentences quoted from the sources.