Quantum Computing for Machine Learning, Electromagnetic Simulation and Applications to EMC Analysis

Speakers:
Qi-jun Zhang, Carleton University, Chancellor’s Professor

Chapter:
German Chapter

Abstract: Quantum computing attracts increasing attention from the computation community in recent years. In certain cases, quantum algorithms has the potential of exponential speedups over their classical counterparts
running on classical computers. Quantum computing opens many new opportunities for solving large-scale problems, and simulating complex physical systems. In this talk, we explore quantum computing for AI/machine learning oriented computations, and application of quantum computing in solving electromagnetic problems. Preliminary exploration of quantum computing for solving electromagnetic field simulation problems have been conducted by researchers, using such as Transmission Line Matrix (TLM), Method of Moments (MoM) and Finite-Element Method (FEM) formulations. We present two types of quantum computing approaches for electromagnetic simulation, one is a complete quantum computing approach, utilizing Harrow–Hassidim–Lloyd (HHL) algorithm; another one is a hybrid classical/quantum computing approach utilizing Variational Quantum Algorithm (VQA). New formulations of electromagnetic equations such as FEM equations, into quantum-compatible format will be described. Methods to prepare electromagnetic field excitation vectors into quantum states will be described. Methods to determine various hyperparameters in quantum computing based electromagnetic simulation will be described. Potential applications for EMC analysis will be explored.