Tailored Quantum Device Calibration with Statistical Model Checking

TL;DR

This paper introduces SPAQ, an extension of the SPA framework, for rigorous statistical evaluation and optimization of quantum device calibration processes using statistical model checking.

Contribution

We extend the SPA framework to SPAQ, enabling probabilistic analysis and optimization of quantum calibration procedures with statistical guarantees.

Findings

SPAQ can determine lower bounds of time to failure in quantum calibration.

SPAQ reveals hidden node dependencies in quantum calibration.

SPAQ helps optimize calibration parameters to improve quantum system availability.

Abstract

Quantum devices require precisely calibrated analog signals, a process that is complex and time-consuming. Many calibration strategies exist, and all require careful analysis and tuning to optimize system availability. To enable rigorous statistical evaluation of quantum calibration procedures, we leverage statistical model checking (SMC), a technique used in fields that require statistical guarantees. SMC allows for probabilistic evaluation of properties of interest, such as a certain parameter's time to failure. We extend the SMC for Processor Analysis (SPA) framework, which uses SMC for evaluation of classical systems, to create SPA for Quantum calibration (SPAQ) enabling simplified tuning and analysis of quantum system calibration. We focus on a directed acyclic graph-based calibration optimization scheme and demonstrate how to craft properties of interest for its analysis. We show how to use SPAQ to find lower bounds of time to failure information, hidden node dependencies, and parameter threshold values and use that information to improve simulated quantum system availability through calibration scheme adjustments.