Superpolynomial quantum-classical separation for density modeling

Article Quantum Science & Technology

On the Quantum versus Classical Learnability of Discrete Distributions

Ryan Sweke et al.

Summary: This study compares the performance of classical and quantum learners for generative modeling within the Probably Approximately Correct (PAC) framework. The results show that quantum learners exhibit a provable advantage over classical learners in efficiently learning certain discrete probability distributions.

QUANTUM (2021)

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Article Physics, Multidisciplinary

A rigorous and robust quantum speed-up in supervised machine learning

Yunchao Liu et al.

Summary: Research shows that heuristic quantum kernel methods can achieve quantum speed-up with only classical data access, and a classification problem is constructed to demonstrate this.

NATURE PHYSICS (2021)

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Review Physics, Applied

Variational quantum algorithms

M. Cerezo et al.

Summary: Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.

NATURE REVIEWS PHYSICS (2021)

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Article Quantum Science & Technology