Real-time Quantum Data Pipeline Optimization

As a forward-thinking SME in the Quantum Computing industry, we face the challenge of processing large volumes of complex quantum data in real time. Our current data pipeline struggles with latency and scalability issues, impacting our ability to deliver timely insights. This project involves re-engineering our data infrastructure to support real-time analytics using Apache Kafka, Spark, and Airflow. You'll work on integrating data mesh architectures and ensuring data observability, to enable seamless data flow from quantum data sources to analytics platforms like Snowflake and BigQuery. The initiative also includes implementing MLOps practices to enhance our machine learning model deployments and performance. We aim to create a robust system that handles event streaming efficiently, reduces data processing time, and provides actionable insights, thereby supporting our strategic goal of maintaining a competitive edge in the fast-evolving quantum computing landscape.