Quanta-Bind: A quantum computing pipeline for strongly correlated systems in metalloproteins

Strongly correlated biomolecular systems such metalloproteins are critical to understanding the important cellular functioning and the pathogenesis of neurodegenerative diseases. Examples include iron-sulfur clusters, believed to be responsible for correct cellular functioning, as well as the amyloid-beta protein aggregating via metal ions such as zinc, iron, or copper and leading to dysfunction. The mechanisms of coordination, dynamics, and other physiological factors in these metalloproteins aren’t fully understood. Quanta-Bind is an algorithmic pipeline combining quantum computing and chemistry techniques to model these interactions better. The pipeline combines the Fragment Molecular Orbital (FMO) method to break down proteins, the Localized Active Space (LAS) and Quantum Bootstrap Embedding (QBE) method to treat correlation, alongside the Generalized Superfast Encoding (GSE) to map fermions to qubits. GSE is a graph-based fermion-to-qubit encoding, providing robustness against errors.

Earlier work demonstrated the algorithmic development of this pipeline and its utility in biomolecular calculations. Here, we emphasize the pipeline’s scalability by demonstrating results of calculations on high performance computers with quantum simulators.