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Title: Entanglement diagnostics for efficient VQA optimization
Abstract We consider information spreading measures in randomly initialized variational quantum circuits and introduce entanglement diagnostics for efficient variational quantum/classical computations. We establish a robust connection between entanglement measures and optimization accuracy by solving two eigensolver problems for Ising Hamiltonians with nearest-neighbor and long-range spin interactions. As the circuit depth affects the average entanglement of random circuit states, the entanglement diagnostics can identify a high-performing depth range for optimization tasks encoded in local Hamiltonians. We argue, based on an eigensolver problem for the Sachdev–Ye–Kitaev model, that entanglement alone is insufficient as a diagnostic to the approximation of volume-law entangled target states and that a large number of circuit parameters is needed for such an optimization task.  more » « less
Award ID(s):
1911298
PAR ID:
10345931
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Statistical Mechanics: Theory and Experiment
Volume:
2022
Issue:
7
ISSN:
1742-5468
Page Range / eLocation ID:
073101
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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