A Comparison of Quantum Computer Architectures in Running Fuzzy Inference Engines

The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.