Tailored Quantum Device Calibration with Statistical Model Checking

2025-07-16

AI Summary

Quantum device analog signal calibration is complex, time-consuming, and lacks systematic statistical evaluation methods. To address this, we propose SPAQ, the first framework to introduce statistical model checking (SMC) into quantum calibration. SPAQ enables probabilistic modeling and formal verification of DAG-structured calibration workflows, capturing critical properties—including parameter thresholds, implicit dependencies, and lower bounds on fault occurrence times. By integrating parameter sensitivity analysis with system availability assessment, SPAQ automates the optimization of calibration strategies. Experimental evaluation demonstrates that SPAQ effectively identifies dominant failure-inducing parameters and latent dependencies, significantly improving calibration efficiency and system availability for analog quantum systems. SPAQ establishes a verifiable, scalable statistical analysis paradigm for quantum hardware calibration.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationSearch and Optimization: Sampling/Simulation-based SearchReasoning under Uncertainty: Stochastic Optimization

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsSecurity and Privacy: Large-scale security measurementsGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Optimizing quantum device calibration for system availability
• Applying statistical model checking to quantum calibration procedures
• Identifying hidden dependencies and thresholds in calibration schemes

Innovation

• Statistical model checking for quantum calibration
• Extended SPA framework to SPAQ
• Directed acyclic graph-based optimization scheme