Quantum machine learning (QML) offers a promising pathway for early adoption in industrial settings. For QML solutions to be effectively implemented in production, dedicated access to quantum hardware is essential. The quantum hardware ecosystem is rapidly evolving, with most of the infrastructure hosted by cloud providers. However, this reliance on cloud services presents challenges, including latency, security concerns, and high costs which can impede real-time applications. Is there a way to circumvent these limitations?
