PreprintCC BY 4.0arXiv
Quantum Imaginary Time Evolution on an Infinite 1D Chain
Abstract
We introduce a quantum-circuit algorithm for performing imaginary-time evolution on infinite one-dimensional lattice systems. The method uses a parameterized quantum circuit to represent a uniform matrix product state ansatz. We derive the ITE algorithm using the time-dependent variational principle and employ the quantum Lanczos algorithm to improve the ground-state energy estimate. As a benchmark, we simulate the transverse-field Ising model using both classical simulators and IBM Quantum devices. Our analysis includes a statistical study of the distributions of the cost function and energy density obtained from quantum measurements, illustrating the effects of finite-sampling noise on convergence.
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BibTeX
@misc{hung2026quantum,
title = {{Quantum Imaginary Time Evolution on an Infinite 1D Chain}},
author = {Hao-Ti Hung and Tung Tsao and Ying-Jer Kao},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.30363},
eprint = {2608.30363},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.30363},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum Imaginary Time Evolution on an Infinite 1D Chain
AU - Hao-Ti Hung
AU - Tung Tsao
AU - Ying-Jer Kao
PY - 2026
DA - 2026/08/31
PB - arXiv
DO - 10.48550/arxiv.2608.30363
UR - https://arxiv.org/abs/2608.30363
AB - We introduce a quantum-circuit algorithm for performing imaginary-time evolution on infinite one-dimensional lattice systems. The method uses a parameterized quantum circuit to represent a uniform matrix product state ansatz. We derive the ITE algorithm using the time-dependent variational principle and employ the quantum Lanczos algorithm to improve the ground-state energy estimate. As a benchmark, we simulate the transverse-field Ising model using both classical simulators and IBM Quantum devices. Our analysis includes a statistical study of the distributions of the cost function and energy density obtained from quantum measurements, illustrating the effects of finite-sampling noise on convergence.
ER -