# Experimental realization of self-guided quantum process tomography

## TL;DR

This paper introduces a self-guided, adaptive quantum process tomography algorithm that efficiently characterizes unknown unitary processes with fewer resources and real-time diagnostics, demonstrated both numerically and experimentally.

## Contribution

The paper presents a novel, fully automated quantum process tomography method optimized for unitary processes, reducing resource requirements and providing real-time feedback.

## Key findings

- Achieves the same 1/n scaling as standard tomography.
- Requires only a single input state and measurement.
- Demonstrated successful experimental implementation for SU(2) processes.

## Abstract

Characterization of quantum processes is a preliminary step necessary in the development of quantum technology. The conventional method uses standard quantum process tomography, which requires $d^2$ input states and $d^4$ quantum measurements for a $d$-dimensional Hilbert space. These experimental requirements are compounded by the complexity of processing the collected data, which can take several orders of magnitude longer than the experiment itself. In this paper we propose an alternative self-guided algorithm for quantum process tomography, tuned for the task of finding an unknown unitary process. Our algorithm is a fully automated and adaptive process characterization technique. The advantages of our algorithm are: inherent robustness to both statistical and technical noise; requires less space and time since there is no post-processing of the data; requires only a single input state and measurement; and, provides on-the-fly diagnostic information while the experiment is running. Numerical results show our algorithm achieves the same $1/n$ scaling as standard quantum process tomography when $n$ uses of the unknown process are used. We also present experimental results wherein the algorithm, and its advantages, are realized for the task of finding an element of $SU(2)$.