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Language models for quantum simulation
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DOI：10.1038/s43588-023-00578-0.png)
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Abstract
En 中文
A key challenge in the effort to simulate today's quantum computing devices is the ability to learn and encode the complex correlations that occur between qubits. Emerging technologies based on language models adopted from machine learning have shown unique abilities to learn quantum states. We highlight the contributions that language models are making in the effort to build quantum computers and discuss their future role in the race to quantum advantage. Language models offer promises in encoding quantum correlations and learning complex quantum states. This Perspective discusses the advantages of employing language models in quantum simulation, explores recent model developments, and offers insights into opportunities for realizing scalable and accurate quantum simulation.
Keywords:
MANY-BODY PROBLEM
NEURAL-NETWORK
COMPUTATIONAL ADVANTAGE
SYSTEMS
STATES
QUBIT
Journal
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18.3
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3.1K
Citations:
4.0K