Quantum Computer Metrics and HPC Center Environmental Sensor Data Analysis Towards Fidelity Prediction

A tight integration of quantum accelerators with high-performance computers (HPC) necessitates relocating the often highly sensitive quantum devices from controlled laboratory environments to noisy data centers. Various factors impacting fidelity must be tracked and mitigated to ensure the performance of quantum computers in such environments. This paper describes a monitoring system utilizing Internet of Things (IoT) technology incorporating multiple sensors to gather and feed data into a data center-wide database. The data collected is then analyzed to identify correlations between fidelity and environmental factors. Using this data, we propose a novel, adaptive calibration mechanism in which a machine learning model predicts the optimal calibration timing for the quantum computer based on fidelity prediction with varying environmental conditions. In the larger scope, this research aims to enhance our understanding of fidelity-altering factors and develop strategies for maintaining optimal quantum computer performance in real-world, noisy data center environments.