Comparing Classical and Quantum Ground State Preparation Heuristics
One promising field of quantum computation is the simulation of quantum systems, and specifically, the task of ground state energy estimation (GSEE). Ground state preparation (GSP) is a crucial component in GSEE algorithms, and classical methods like Hartree-Fock state preparation are commonly used....
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
10.01.2024
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Subjects | |
Online Access | Get full text |
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Summary: | One promising field of quantum computation is the simulation of quantum
systems, and specifically, the task of ground state energy estimation (GSEE).
Ground state preparation (GSP) is a crucial component in GSEE algorithms, and
classical methods like Hartree-Fock state preparation are commonly used.
However, the efficiency of such classical methods diminishes exponentially with
increasing system size in certain cases. In this study, we investigated whether
in those cases quantum heuristic GSP methods could improve the overlap values
compared to Hartree-Fock. Moreover, we carefully studied the performance gain
for GSEE algorithms by exploring the trade-off between the overlap improvement
and the associated resource cost in terms of T-gates of the GSP algorithm. Our
findings indicate that quantum heuristic GSP can accelerate GSEE tasks, already
for computationally affordable strongly-correlated systems of intermediate
size. These results suggest that quantum heuristic GSP has the potential to
significantly reduce the runtime requirements of GSEE algorithms, thereby
enhancing their suitability for implementation on quantum hardware. |
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DOI: | 10.48550/arxiv.2401.05306 |