Superconductivity in the $t$-$t'$ Hubbard Model from Symmetry-Preserving Neural-Network Quantum States
A preprint on arXiv (quant-ph) reports the use of symmetry-preserving neural-network quantum states to simulate the t-t' Hubbard model. The authors find superconducting ground states in this model, a long-standing challenge in strongly correlated electron physics. The symmetry constraints are imposed to improve the accuracy of the neural-network ansatz.
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What this could mean
- 0–2 yearsPlausible
This approach could provide a reliable classical benchmark for superconducting phases of the Hubbard model, enabling validation of quantum simulators and variational quantum algorithms within two years.
Symmetry projection reduces the biases that previously limited NQS accuracy for superconducting order, and near-term quantum devices are increasingly targeting small Hubbard systems where exact classical references are scarce.
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