Theoretical framework establishes rigorous generalization error bounds for Transformer-based neural quantum states under in-context learning, proving prediction error decreases inversely with context examples and network depth.

#QuantumMachineLearning #NeuralQuantumStates #TransformerTheory

A single autoregressive neural model now learns molecular ground states from sparse anchor geometries, achieving chemical accuracy at untrained configurations with 25.8× GPU-cost reduction compared to independent optimization.

#NeuralQuantumStates #MolecularChemistry #Research

Bridging quantum computing and AI: Neural embeddings boost quantum ML accuracy on real hardware; margin theory explains generalization; Mamba decoder scales QEC decoding from O(d⁴) to O(d²) complexity for fault-tolerant quantum computers.

#QuantumML #QuantumErrorCorrection #NeuralQuantumStates

Identifies geometric inflation of QGT null-space rotation as root cause of NQS accuracy failures in quantum dynamics. Proposes mean null-space rotation angle as diagnostic tool to predict and prevent inaccuracy in time evolution algorithms.

#QuantumAlgorithms #NeuralQuantumStates #QuantumSimulation

Interpolation sampling method enables neural quantum states to accurately propagate quantum dynamics from extremely sparse initial states, overcoming fundamental limitations that previously made such simulations intractable.

#NeuralQuantumStates #QuantumSimulation #Research