This is the code for the paper arXiv:2405.03754.
The first step is to compute the initial state. This can be done with DMRG in the file dmrg.py using tenpy. Otherwise, a random initial state will be provided.
The second part of the algorithm is to compute the Fourier moments of the target Hamiltonian dynamics_pl.py and dyncamics_qsim.py scripts, which rely on pennylane and qsimcirq, respectively. In these scripts we consider the fully connected Heisenberg model and the evolution is performed with Trotterization via a swap network. The latter also supports GPU execution.
The bulk of the algorithm can be run using the main.py, which computes the Fourier decomposition (present in the algorithms/Fk.py), samples from the Fourier moments, and builds the estimator. The CDF for 26 spins fully connected Hamiltonian using low-bond dimension initial is displayed below.
The steps are automatically detected within the main script through the algorithms/trendfliter.py. The step detection is better illustrated in the notebook resources_comparison.ipynb, which estimates the number of samples required to detect a step of a given size.
If you use this work for your research, please cite us!
@misc{EFTQC_practice, title = "Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation", author = {Oriel Kiss and Utkarsh Azad and Borja Requena and Alessandro Roggero and David Wakeham and Juan Miguel Arrazola }, year={2024}, month = {5}, eprint={2405.03754}, archivePrefix={arXiv}, primaryClass={quant-ph} }