Adaptive Localized Quantum Subspace Methods for Large-Scale Molecular Electronic Structure Simulation
Develop a scalable hybrid quantum–classical framework for electronic-structure simulation of large and strongly correlated molecular systems using sample-based quantum diagonalization and localized active-space techniques.
The goal is not simply to reproduce an existing algorithm, but to build an adaptive, resource-aware quantum chemistry workflow on top of recent state-of-the-art methods, benchmark it against classical approaches, and investigate pathways toward increasingly large molecular simulations.
Tools-Technologies | C, Jupyter Python Notebooks, PowerAI, Spark |
Platform | 1 ) OpenPOWER/Power
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College | All College |
richa goel' Comment
the project aims to answer:
Can quantum-generated electronic configurations be combined with adaptive active-space selection and classical determinant expansion to reduce the quantum resources required for accurate electronic-structure calculations as molecular size increases?