Multi-stage tomography based on eigenanalysis for high-dimensional dense unitary processes in gate-based quantum computers

TL;DR

This paper introduces multi-stage eigenanalysis-based quantum process tomography methods for high-dimensional dense unitary processes, enabling efficient characterization of complex quantum gates in large-scale quantum computers.

Contribution

It develops novel multi-stage QPT algorithms that improve accuracy and scalability for high-dimensional quantum processes, extending existing eigenanalysis techniques.

Findings

Single-stage methods apply to up to 13 qubits on standard hardware.

Multi-stage methods achieve higher accuracy in process estimation.

Algorithms are validated through simulations demonstrating effectiveness.

Abstract

Quantum Process Tomography (QPT) methods aim at identifying, i.e. estimating, a quantum process. QPT is a major quantum information processing tool, since it especially allows one to experimentally characterize the actual behavior of quantum gates, that may be used as the building blocks of quantum computers. We here consider unitary, possibly dense (i.e. without sparsity constraints) processes, which corresponds to isolated systems. Moreover, we develop QPT methods that are applicable to a significant number of qubits and hence to a high state space dimension, which allows one to tackle more complex problems. Using the unitarity of the process allows us to develop methods that first achieve part of QPT by performing an eigenanalysis of the estimated density matrix of a process output. Building upon this idea, we first develop a class of complete algorithms that are single-stage, in the sense that they use only one eigendecomposition. We then extend them to multiple-stage algorithms (i.e. with several eigendecompositions), in order to address high-dimensional state spaces while being less limited by the estimation errors made when using an arbitrary given Quantum State Tomography (QST) algorithm as a building block of our overall methods. We first propose two-stage methods and we then extend them to dichotomic methods, whose number of stages increases with the considered state space dimension. The relevance of our methods is validated with simulations. Single-stage and two-stage methods efficiently apply up to 13 qubits on a standard PC (with 16 GB of RAM). Multi-stage methods yield an even higher accuracy.