The Scalability of Quantum Computing Models to Classical Computers
Abstract
Quantum Machine Learning (QML) Models are models that run on Quantum Computing platforms that offer tremendous performance benefits over Traditional Computer Systems. While Quantum Models are significantly more powerful than classical computers, they come with some serious limitations such as needing to be kept at extremely low temperatures, a need for extreme atmospheric stability, and most importantly, their particularly high costs. This paper explores some ways to scale Quantum Systems to be more accessible for everyday users, discussing solutions such as Hybrid Quantum-Classical models, Improved Qubit Technology, and Cloud Quantum Systems, and finding that the most optimal solution to scale Quantum Computing is with a joint approach between Hybrid Models as well as Cloud Infrastructure. This solution is able to utilize the best capabilities of both Quantum Computers and Classical Computers, allowing users to employ the most advanced Quantum Computers hosted on the cloud, alongside their classical computer in order to achieve performance results similar to those achieved by Quantum Models today.
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