Fast generation of Pauli transfer matrices utilizing tensor product structure

TL;DR

This paper introduces new tensor product-based algorithms for efficiently converting various quantum channel representations into Pauli transfer matrices, improving computational performance for multi-qubit systems.

Contribution

The authors develop algorithms that explicitly utilize tensor product structure to convert quantum channels into Pauli transfer matrices more efficiently than existing methods.

Findings

Algorithms demonstrate improved runtime performance.

Effective for systems with up to seven qubits.

Asymptotic scaling derived and validated.

Abstract

Analysis of quantum processes, especially in the context of noise, errors, and decoherence is essential for the improvement of quantum devices. An intuitive representation of those processes modeled by quantum channels are Pauli transfer matrices. They display the action of a linear map in the $n$-qubit Pauli basis in a way, that is more intuitive, since Pauli strings are more tangible objects than the standard basis matrices. We set out to investigate classical algorithms that convert the various representations into Pauli transfer matrices. We propose new algorithms that make explicit use of the tensor product structure of the Pauli basis. They convert a quantum channel in a given representation (Chi or process matrix, Choi matrix, superoperator, or Kraus operators) to the corresponding Pauli transfer matrix. Moreover, the underlying principle can also be used to calculate the Pauli transfer matrix of other linear operations over $n$-qubit matrices such as left-, right-, and sandwich multiplication as well as forming the (anti-)commutator with a given operator. Finally, we investigate the runtime of these algorithms, derive their asymptotic scaling and demonstrate improved performance using instances with up to seven qubits.