aiqbench analysis
This research presents a novel quantum-circuit algorithm for imaginary-time evolution on infinite 1D lattice systems, utilizing matrix product state ansatzes to estimate ground-state energies. By benchmarking the transverse-field Ising model on IBM Quantum devices, the study provides critical insights into how finite-sampling noise impacts convergence and energy density distributions. The work advances practical quantum simulation techniques for handling large-scale lattice problems without relying solely on classical supercomputers.
Quantum Imaginary Time Evolution on an Infinite 1D Chain
arXiv:2608.30363v1 Announce Type: new 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.
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.
arXiv:2608.30363 [quant-ph]
(or arXiv:2608.30363v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.30363 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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