AI and the World's Hard Problems · Quantum physics & quantum computing · arXiv · 2026-10-22
Mamba Quantum Eigensolver (MQE): A Selective State Space Model for Scalable Ground State Simulations in Quantum Chemistry
Predicting molecular electronic structure with quantum computing is a challenge in computational chemistry: variational quantum eigensolvers are hindered by optimization difficulties, while the generative quantum eigensolver (GQE) is bottlenecked by the quadratic computational complexity of its Transformer backbone. The Mamba Quantum Eigensolver (Mamba-QE) replaces the Transformer with a selective state space model that processes operator sequences with linear O(L) complexity, and adds a distance-conditioned mechanism to directly predict complete potential energy surfaces. Evaluated on the H₂ molecule, it reproduces FCI reference energies well within chemical accuracy, with energy error below 3×10⁻⁶ Ha across 16 geometries and zero variational violations.