svPITE: A Python package for the state-vector-based probabilistic imaginary-time evolution algorithm

TL;DR

svPITE is a Python package that implements a probabilistic imaginary-time evolution algorithm for ground-state preparation, supporting shot-based simulation, benchmarking, and integration with other tools for spectral function computation.

Contribution

The package provides a state-vector-based implementation of the probabilistic imaginary-time evolution algorithm with benchmarking and interoperability features.

Findings

Supports efficient parameter tuning for the algorithm

Enables real-time evolution and spectral function calculations

Facilitates benchmarking against exact diagonalisation

Abstract

We present a Python package for ground-state preparation based on the probabilistic imaginary-time evolution algorithm, with particular focus on its state-vector-based implementation. A standard shot-based simulation is also supported, and results can be benchmarked against exact diagonalisation via a dedicated wrapper. The package enables efficient tuning of initial parameters, facilitating systematic exploration and optimisation of the method's performance. Starting from the prepared ground state, the strong interoperability with other packages further enables real-time evolution and the computation of spectral functions, such as the spin-spin dynamical structure factor.