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While impressive progress has been already achieved in wide-bandgap (WBG)semicon-ductors such as 4H-SiC and GaN technologies, the lack of intelligentmethodologies to control the gate drives prevented to the exploit the maximumpotential of semiconductor chips, from obtaining the desired devicesoperations. Thus, a potent ongoing trend is to design a fast gate driverswitching scheme to upgrade the performance of electronic equipment at thesystem level. To address this issue, this work proposes a novel intelligentscheme for the control of gate driver switching using the concept of quantumcomputation in machine learning. In particular, the quantum principle isincorporated into deep reinforcement learning (DRL) to address the hardwarelimitations of con-ventional computers and the growing amount of data sets.Taking potential benefits of quantum theory, the DRL algorithm influenced byquantum specifications (referred to as QDRL) will not only ameliorate theperformance of the native algorithm on traditional computers, but also enhancethe progress of relevant research fields like quantum computing and machinelearning. To test the prac-ticability and usefulness of QDRL, a dc/dc parallelboost converters feeding constant power loads (CPLs) is chosen as the casestudy, and several Power Hard-ware-in-the-loop (PHiL) experiments andcomparative analysis are given.

Quantum Power Electronics: From Theory to Implementation