ArticleNPJ quantum information2026
Exponential quantum advantages in learning quantum observables from classical data.
Article in NPJ quantum information, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Quantum-machine-assisted drug discovery.npj drug discovery · 2026Review
- Quantum advantage for learning shallow neural networks with natural data distributions.Nature communications · 2025Article
- Efficient learning for linear properties of bounded-gate quantum circuits.Nature communications · 2025Article
- Unveiling quantum phase transitions from traps in variational quantum algorithms.NPJ quantum information · 2025Article
- Parameterized quantum circuits as universal generative models for continuous multivariate distributions.NPJ quantum information · 2025Article
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3 authors.
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Abstract
Quantum computers are believed to bring computational advantages in simulating quantum many-body systems. However, recent works have shown that classical machine learning algorithms are able to predict numerous properties of quantum systems with classical data. Despite examples of learning tasks with provable quantum advantages being proposed, they all involve cryptographic functions and do not represent any physical scenarios encountered in laboratory settings. In this paper, we prove quantum advantages for the physically relevant task of learning quantum observables from classical (measured-out) data. We consider two types of observables: first, we prove a learning advantage for linear combinations of Pauli strings, then we extend our results to a broader case of unitarily parametrized observables. For each case, we delineate sharp boundaries separating physically relevant tasks that admit efficient classical learning from those for which a quantum computer remains necessary for data analysis. Unlike previous works, our classical hardness results rely only on the weaker assumption that
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