BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Berlin BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261025T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260516T111247Z UID:8A62C574-A8E3-4A35-A93E-982C28AB26DF DTSTART;TZID=Europe/Berlin:20260513T130000 DTEND;TZID=Europe/Berlin:20260513T143000 DESCRIPTION:Electromagnetic Compatibility Distinguished Lecturer Talk by Pr of. Qi-Jun Zhang (Carleton University\, Canada)\n\nQuantum Computing for M achine Learning\, Electromagnetic Simulation and Applications to EMC Analy sis\n\nAbstract: Quantum computing attracts increasing attention from the computation community in recent years. In certain cases\, quantum algorith ms has the potential of exponential speedups over their classical counterp arts running on classical computers. Quantum computing opens many new oppo rtunities for solving large-scale problems\, and simulating complex physic al systems. In this talk\, we explore quantum computing for AI/machine lea rning oriented computations\, and application of quantum computing in solv ing 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 tw o types of quantum computing approaches for electromagnetic simulation\, o ne is a complete quantum computing approach\, utilizing Harrow–Hassidim –Lloyd (HHL) algorithm\; another one is a hybrid classical/quantum compu ting approach utilizing Variational Quantum Algorithm (VQA). New formulati ons of electromagnetic equations such as FEM equations\, into quantum-comp atible format will be described. Methods to prepare electromagnetic field excitation vectors into quantum states will be described. Methods to deter mine various hyperparameters in quantum computing based electromagnetic si mulation will be described. Potential applications for EMC analysis will b e explored.\n\nBiography: Dr. Qi.jun Zhang received the B.Eng. degree in E E from Nanjing University of Science and Technology (Nanjing) in 1982\, an d the Ph\,D. degree in EE from McMaster University (Hamilton\, Ontario) in 1987. He was a researcher engineer in Optimization Systems Associates Inc . (Dundas\, Ontario) during 1988-1990 developing advanced microwave optimi zation software. He joined the Department of Electronics\, Carleton Univer sity in 1990\, where he is currently a Chancellor’s Professor.\nDr. Zhan g’s research area is AI/machine learning\, and optimization for designin g high-speed/high-frequency components/packages and systems\, which are bu ilding blocks of computers\, wireless and wired systems in telecommunicati ons\, internet\, and intelligent and autonomous systems. He also advanced the theory and practice of microwave active and passive computer-aided des ign with innovations in linear and nonlinear device modeling and circuit o ptimization for which he received the IEEE Fellowship in 2006.\nDr. Zhang is one of the pioneers of neural networks and machine learning for microwa ves which started in the early 1990s. Dr. Zhang’s continuous innovations in this area for over 30 years have established many research milestones\ , and contributed to the substantial development of the area. He authored (with Prof. K.C. Gupta) the first book of the area (Neural Networks for RF and Microwave Design\, Boston\, Artech House\, 2000)\, and developed the first software (NeuroModeler\, 1998) of the area. His neural network based modeling technology was adopted in industrial projects such as the Advanc ed Embedded Passives Technology Consortium (2000-2003) funded by the US De partment of Commerce and the National Center for Manufacturing Sciences (M ichigan\, USA). In 2012\, neural network based transistor modeling technol ogy pioneered by him and his student became a primary feature of Agilent/K eysight IC-CAP software\, the microwave industry’s dominant modeling too l. Over 360 technical papers archive his pioneering contributions.\n\nRoom : 11\, Bldg: 09\, Universitätsplatz 2\, Magdeburg\, Sachsen-Anhalt\, Germ any\, 39106\, Virtual: https://events.vtools.ieee.org/m/558674 LOCATION:Room: 11\, Bldg: 09\, Universitätsplatz 2\, Magdeburg\, Sachsen-A nhalt\, Germany\, 39106\, Virtual: https://events.vtools.ieee.org/m/558674 ORGANIZER:cheng.yang@tuhh.de SEQUENCE:11 SUMMARY:EMC Distinguished Lecture Prof. Qi-jun Zhang at Otto von Guericke U niversity Magdeburg URL;VALUE=URI:https://events.vtools.ieee.org/m/558674 X-ALT-DESC:Description: <br /><p>Electromagnetic Compatibility Distinguishe d Lecturer Talk by Prof. Qi-Jun Zhang (Carleton University\, Canada)</p>\n <p><em>Quantum Computing for Machine Learning\, Electromagnetic Simulation and Applications to EMC Analysis</em></p>\n<p><strong>Abstract:</strong> Quantum computing attracts increasing attention from the computation commu nity in recent years. In certain cases\, quantum algorithms has the potent ial of exponential speedups over their classical counterparts running on c lassical computers. Quantum computing opens many new opportunities for sol ving large-scale problems\, and simulating complex physical systems. In th is talk\, we explore quantum computing for AI/machine learning oriented co mputations\, and application of quantum computing in solving electromagnet ic problems. Preliminary exploration of quantum computing for solving elec tromagnetic field simulation problems have been conducted by researchers\, using such as Transmission Line Matrix (TLM)\, Method of Moments (MoM) an d Finite-Element Method (FEM) formulations. We present two types of quantu m computing approaches for electromagnetic simulation\, one is a complete quantum computing approach\, utilizing Harrow&ndash\;Hassidim&ndash\;Lloyd (HHL) algorithm\; another one is a hybrid classical/quantum computing app roach utilizing Variational Quantum Algorithm (VQA). New formulations of e lectromagnetic equations such as FEM equations\, into quantum-compatible f ormat will be described. Methods to prepare electromagnetic field excitati on vectors into quantum states will be described. Methods to determine var ious hyperparameters in quantum computing based electromagnetic simulation will be described. Potential applications for EMC analysis will be explor ed.</p>\n<p><strong>Biography:</strong> Dr. Qi.jun Zhang received the B.En g. degree in EE from Nanjing University of Science and Technology (Nanjing ) in 1982\, and the Ph\,D. degree in EE from McMaster University (Hamilton \, Ontario) in 1987. He was a researcher engineer in Optimization Systems Associates Inc. (Dundas\, Ontario) during 1988-1990 developing advanced mi crowave optimization software. He joined the Department of Electronics\, C arleton University in 1990\, where he is currently a Chancellor&rsquo\;s P rofessor.<br>Dr. Zhang&rsquo\;s research area is AI/machine learning\, and optimization for designing high-speed/high-frequency components/packages and systems\, which are building blocks of computers\, wireless and wired systems in telecommunications\, internet\, and intelligent and autonomous systems. He also advanced the theory and practice of microwave active and passive computer-aided design with innovations in linear and nonlinear dev ice modeling and circuit optimization for which he received the IEEE Fello wship in 2006.<br>Dr. Zhang is one of the pioneers of neural networks and machine learning for microwaves which started in the early 1990s. Dr. Zhan g&rsquo\;s continuous innovations in this area for over 30 years have esta blished many research milestones\, and contributed to the substantial deve lopment of the area. He authored (with Prof. K.C. Gupta) the first book of the area (Neural Networks for RF and Microwave Design\, Boston\, Artech H ouse\, 2000)\, and developed the first software (NeuroModeler\, 1998) of t he area. His neural network based modeling technology was adopted in indus trial projects such as the Advanced Embedded Passives Technology Consortiu m (2000-2003) funded by the US Department of Commerce and the National Cen ter for Manufacturing Sciences (Michigan\, USA). In 2012\, neural network based transistor modeling technology pioneered by him and his student beca me a primary feature of Agilent/Keysight IC-CAP software\, the microwave i ndustry&rsquo\;s dominant modeling tool. Over 360 technical papers archive his pioneering contributions.</p> END:VEVENT END:VCALENDAR