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The paper applies a Selective State Space Model (Mamba) inside the Generative Quantum Eigensolver to cut operator-sequence processing from quadratic to linear cost. Its evidence so far is limited to the H2 molecule, so the scaling claim for larger molecules remains untested, and the article itself raises that open question. Readers should watch for benchmarks on bigger systems and independent replication before treating this as a practical route to quantum chemistry.
Researchers Predict Stable Electronic Structures Using Mamba
A computational bottleneck previously limited quantum chemistry to molecules that could be simulated with quadratic scaling, until now. Replacing conventional methods, Mamba Quantum Eigensolver achieves linear complexity in simulating ground states and predicts complete potential energy surfaces directly. Tested on the H2 molecule, it delivers results with an energy error below 3×10−6 Ha across sixteen different structures without any variational violations.
Predicting molecular electronic structure remains a key challenge in computational chemistry; current methods struggle with both optimisation and scalability. Mamba Quantum Eigensolver, or Mamba-QE, uses a new approach to processing operator sequences within the Generative Quantum Eigensolver framework. Replacing a traditional Transformer component with a Selective State Space Model reduces computational complexity from quadratic scaling to linear while accurately modelling the H2 molecule’s potential energy…
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