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Quantum Hamiltonian Embeddings Framework Introduced for Supervised Learning

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a novel supervised learning framework called quantum Hamiltonian embeddings, which encodes classical datasets into a parameterized Hamiltonian. The framework optimizes this data-dependent Hamiltonian, with its approximate ground state defining the classification rule. This approach offers theoretical guarantees on performance and generalization, and has been demonstrated at scale on IBM Heron processors. AI

IMPACT This framework could enable quantum computers to perform machine learning tasks beyond the capabilities of classical algorithms.

RANK_REASON The cluster contains an arXiv preprint detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum Hamiltonian Embeddings Framework Introduced for Supervised Learning

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The cluster contains an arXiv preprint detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Riccardo Molteni, Minjun Jeon, Paolo Braccia, Abhinav Deshpande, Vedran Dunjko, Liang Jiang, Su-un Lee, Swarnadeep Majumder, Javier Robledo Moreno, Parthasarathy Suryanarayanan, Kunal Sharma ·

    Supervised Learning with Quantum Hamiltonian Embeddings

    arXiv:2610.26833v2 Announce Type: replace-cross Abstract: 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, tra…

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