Efficiently manipulating Pauli strings with PauliArray

TL;DR

The paper introduces PauliArray, a NumPy-based library that efficiently manipulates large sets of Pauli strings, streamlining quantum computing tasks like fermion-to-qubit mapping and expectation value estimation.

Contribution

It presents a versatile, high-performance library for handling Pauli strings, improving speed and ease of use over existing tools in quantum computing applications.

Findings

PauliArray enables faster manipulation of Pauli strings.

It simplifies complex quantum operations like commutator calculations.

The library improves efficiency in quantum algorithms involving Pauli operators.

Abstract

Pauli matrices and Pauli strings are widely used in quantum computing. These mathematical objects are useful to describe or manipulate the quantum state of qubits. They offer a convenient basis to express operators and observables used in different problem instances such as molecular simulation and combinatorial optimization. Therefore, it is important to have a well-rounded, versatile and efficient tool to handle a large number of Pauli strings and operators expressed in this basis. This is the objective behind the development of the PauliArray library presented in this work. This library introduces data structures to represent arrays of Pauli strings and operators as well as various methods to modify and combine them. Built using NumPy, PauliArray offers fast operations and the ability to use broadcasting to easily carry out otherwise cumbersome manipulations. Applications to the fermion-to-qubit mapping, to the estimation of expectation values and to the computation of commutators are considered to illustrate how PauliArray can simplify some relevant tasks and accomplish them faster than current libraries.