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Quantum Occam Learning theory links circuit complexity to data samples

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new theoretical framework called Quantum Occam Learning to address the expressibility of quantum machine learning models. This framework focuses on how well a quantum model can represent data when learned from a finite number of quantum state samples. The work establishes a sample-supported expressibility law, indicating that the number of gates in a quantum circuit is a statistical resource limited by the available data. AI

RANK_REASON This is a theoretical computer science paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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COVERAGE [1]

  1. 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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