Abstract Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. In the regime of strong entanglement, the quantum computer provides correct results for which leading classical approximations such as pure-state-based 1D (matrix product states, MPS) and 2D (isometric tensor network states, isoTNS) tensor network methods 2,3 break down.
This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.
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| Category | 🤖 Artificial Intelligence |
| Published | Jun 14, 2023 |
| Journal | Nature |
| Authors | Young‐Seok Kim, Andrew Eddins, Sajant Anand, Ken Xuan Wei, E. van den Berg |
| DOI | 10.1038/s41586-023-06096-3 |
| Citations | 1,000 |
| Source | OpenAlex |