Experimental Efficient Influence Sampling of Quantum Processes

TL;DR

This paper introduces influence sampling, a scalable quantum process characterization method that uses only single-qubit gates, enabling efficient analysis of large quantum systems and reducing resource requirements.

Contribution

The paper presents influence sampling, a novel protocol that efficiently estimates quantum process influence on all qubit subsets with minimal resources, scalable to large systems.

Findings

Successfully applied to a 24-qubit system

Identified high-influence qubits and reduced processes to smaller approximations

Validated the approach with a two-qubit process

Abstract

Characterizing quantum processes is essential for unlocking the potential of quantum devices. However, standard quantum process tomography is resource-intensive and becomes infeasible on large-scale systems. Despite alternative approaches have been successfully developed for specific scenarios, they typically rely on multi-qubit gates or extensive prior knowledge, limiting their practicability and scalability. To address these challenges and complement existing approaches, we introduce $\textit{influence sampling}$, an efficient and scalable protocol that quantifies the $\textit{influence}$ of a quantum process on all qubit subsets using only single-qubit test gates, with sample complexity independent of system size. Using a photonic platform, we demonstrate influence sampling to identify high-influence qubits, reduce the full process to a smaller effective process, i.e., a junta approximation, and then learn it. We further confirm scalability by applying the protocol to a 24-qubit system and validate the junta approximation on a two-qubit process. These results establish influence sampling as a critical characterization technique, facilitating process learning and device assessment.