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Quantum Occam Learning framework advances quantum model expressibility

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called Quantum Occam Learning to address the expressibility of quantum machine learning models. This theory focuses on how well a quantum model can represent data when learned from a finite number of quantum state copies. The framework establishes a sample-supported expressibility law, indicating that the number of gates a model can support is limited by the number of samples and desired accuracy. AI

IMPACT Establishes a theoretical limit on quantum model expressibility based on data samples, guiding future quantum ML research.

RANK_REASON This is a research paper detailing a new theoretical framework for quantum machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jeongho Bang, Kyoungho Cho, Jeongwoo Jae ·

    Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning

    arXiv:2606.12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learned from fi…

  2. arXiv cs.LG TIER_1 English(EN) · Jeongwoo Jae ·

    Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning

    A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learned from finitely many copies of an unknown quantum state. In…

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