# Preprint reports neural model for larger quantum ground-state evaluations

_Monday, August 24, 2026 at 12:00 AM EDT · Science, AI · Latest · Tier 2 — Notable_

Authors of a new arXiv preprint report Hamilton-Zero, a roughly 0.5-billion-parameter neural tensor-network model trained across hundreds of thousands of quadratic qubit Hamiltonians. They say they trained it on systems up to 64 qubits, fine-tuned it on held-out systems up to 1,024 qubits, and evaluated it on systems up to 8,100 qubits. The work represents quantum ground-state learning as optimization over functions on SU(2) rather than explicit Hilbert-space amplitudes.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2608.11911)

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