REVIEW 1 cited by
Matrix product state ansatz for the variational quantum solution of the Heisenberg model on Kagome geometries
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The Variational Quantum Eigensolver (VQE) algorithm, as applied to finding the ground state of a Hamiltonian, is particularly well-suited for deployment on noisy intermediate-scale quantum (NISQ) devices. Here we utilize the VQE algorithm with a quantum circuit ansatz inspired by the Density Matrix Renormalization Group (DMRG) algorithm. To ameliorate the impact of realistic noise on the performance of the method we employ zero-noise extrapolation. We find that, with realistic error rates, our DMRG-VQE hybrid algorithm delivers good results for strongly correlated systems. We illustrate our approach with the Heisenberg model on a Kagome lattice patch and demonstrate that DMRG-VQE hybrid methods can locate, and faithfully represent the physics of, the ground state of such systems. Moreover, the parameterized ansatz circuit used in this work is low-depth and requires a reasonably small number of parameters, so is efficient for NISQ devices.
Forward citations
Cited by 1 Pith paper
-
Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation
MPS pre-training, neural-network-assisted zero-noise extrapolation, and Pauli grouping combine to give simulated H4 ground-state energies within about 0.02 Hartree of the FCI benchmark under a specific noise model.
Discussion (0). Continue with ORCID to comment.