Hardware Agnostic Backend Adaptation through Quantum Circuit Evaluation

Quantum development platforms usually provide a choice of numerous simulators and quantum processing units (QPUs) for users to choose from and users are still forced to choose and configure a backend manually. As backend collections increase in size, the manual process consists of speculation or manual exploration with limitations in terms of performance and scalability. The approach specified in this paper, although applicable for major platforms, focuses on Qniverse,an unified quantum computing platform that has an integrated working environment for quantum circuit design and execution but still back ends are to be selected manually. The limitations of backend selection through manual conclusion are that novice users are affected and there are complexities in executing the operations. This paper introduces a hardware agnostic backend adaptation that automates the backend selection using circuit analyses and multi criteria backend ranking. The proposed model has three major modules. First is the Circuit Analysis Engine that identifies quantum circuit structure in terms of number of qubits, circuit depth and component gates. Second is a Backend Capability Mapping module that stores each backend capability details in structured forms. Third is an Intelligent Backend Recommendation Algorithm that specifically selects and weighs backend candidates according to latency time, accuracy level, cost constraints and resource allocation. All three unite to form an integrated quantum middleware adoption.