Near-Minimal Gate Set Tomography Experiment Designs

TL;DR

This paper introduces a method to optimize gate set tomography experiments by removing redundancies, resulting in smaller, scalable designs that maintain high precision, demonstrated through simulations and theoretical analysis.

Contribution

It presents a systematic approach to streamline GST experiment designs by analyzing gate sensitivities, enabling near-minimal, scalable experiments without sacrificing accuracy.

Findings

Streamlined two-qubit GST experiments with minimal circuits.

Achieved Heisenberg-like scaling in precision through optimized designs.

Potential extension of techniques to three-qubit systems.

Abstract

Gate set tomography (GST) provides precise, self-consistent estimates of the noise channels for all of a quantum processor's logic gates. But GST experiments are large, involving many distinct quantum circuits. This has prevented their use on systems larger than two qubits. Here, we show how to streamline GST experiment designs by removing almost all redundancy, creating smaller and more scalable experiments without losing precision. We do this by analyzing the "germ" subroutines at the heart of GST circuits, identifying exactly what gate set parameters they are sensitive to, and leveraging this information to remove circuits that duplicate other circuits' sensitivities. We apply this technique to two-qubit GST experiments, generating streamlined experiment designs that contain only slightly more circuits than the theoretical minimum bounds, but still achieve Heisenberg-like scaling in precision (as demonstrated via simulation and a theoretical analysis using Fisher information). In practical use, the new experiment designs can match the precision of previous GST experiments with significantly fewer circuits. We discuss the prospects and feasibility of extending GST to three-qubit systems using our techniques.