Hamilton-Zero: Neural Foundation Model for Quantum Ground States

Researchers have introduced Hamilton-Zero, a neural tensor-network foundation model designed to compute ground states of arbitrary quadratic qubit Hamiltonians. The model, detailed in a paper on arXiv (2608.11911), leverages approximately 0.5 billion variational parameters and is trained using techniques from large language models and deep reinforcement learning. The approach reformulates spin-1/2 quantum ground-state learning as manifold variational optimization over centrally odd scalar functions on SU(2)^N, replacing explicit Hilbert-space vector amplitudes with manifold functions acted upon by Lie derivatives. The authors prove that this variational principle preserves the ground-state upper bound for the spin-1/2 sector. This work addresses the challenge of quantum advantage by amortizing the problem across a universal set of Hamiltonians, potentially enabling classical simulation of systems previously thought intractable.