A Quantum-classical Hybrid Clustering Algorithm for Excited States Preparation - RMGS
Project: Research
Researcher(s)
Description
Quantum computers have been widely speculated to offer significant advantages in obtaining the eigenstates of many-body Hamiltonians in chemistry and physics. In this proposal, we propose a quantum-classical hybrid strategy for quick eigenspectrum preparation. Different from the typical setting of performing the accurate but cost-consuming excited state finding method, we extend the imaginary-time method to the quantum-classical hybrid clustering for the rough preparation of the eigenspectrum. To make the method accessible in the noisy intermediate-scale quantum era, we plan to use a type of measurement-friendly parameterized quantum circuit where parameters scale linearly with the system as a kernel to extract eigenspectrum information for following classical clustering like K-means. Through numerical examples and theoretical analysis, we expect to show that the quantum-classical hybrid clustering can obtain the eigenspectrum distribution more efficiently than the existing excited state methods. And, it can be used as a subroutine to speed up existing classical or quantum methods to approximately find exact eigenstates.Detail(s)
Project number | 9229135 |
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Grant type | DON_RMG |
Status | Active |
Effective start/end date | 1/06/23 → … |