Quantum Fuzzy Inference Engine for Particle Accelerator Control - Giovanni Acampora; Michele Grossi; Michael Schenk; Roberto Schiattarella (IEEE Transactions on Quantum Engineering)
Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.