Predicting the Optimizability for Workflow Decisions

With the rapid advancement of quantum technologies, the integration between classical and quantum computing systems is an active area of research critical to future development. The coupling between these systems requires both to be as efficient as possible. One of the key elements to increase efficiency on the quantum side is circuit optimization. The goal is to execute the circuit on the desired hardware in less time and with less complexity, thereby reducing the impact of noise on the quantum system. However, the optimization process does not guarantee to generate improved results, yet it is always a computationally highly complex task that can create significant load for the classical computing side. To mitigate this problem, we propose a novel approach to predict the optimizability of any circuit using a Machine Learning-based algorithm within the decision workflow. This optimizes the most suitable circuits thereby increasing efficiency of the optimization process itself.