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IEEE MTT-S International Microwave Symposium digest

On the Possibility of Quantum Simulation of Electromagnetic Structures

Johannes A. Russer, Michael Haider, Christian Jirauschek, P. Russer

Published Jun 1, 2019• 12 citations
Publisher

Abstract

The prospects of quantum simulation and design of complex electromagnetic structures is discussed. Quantum superposition and entanglement has the potential of high parallelism and efficiency in CAD. The transmission line matrix (TLM) scheme for EM field simulation provides a basis for a quantum algorithm for EM field modeling. The implementation and problems to be solved are discussed.

DOI
10.1109/mwsym.2019.8700886

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The prospects of quantum simulation and design of complex electromagnetic structures is discussed. Quantum superposition and entanglement has the potential of high parallelism and efficiency in CAD. The transmission line matrix (TLM) scheme for EM field simulation provides a basis for a quantum algorithm for EM field modeling. The implementation and problems to be solved are discussed.
Quantum simulation of electromagnetic (EM) structures is still in its infancy. In this paper, we investigate the possibility of applying quantum computing to solve the finite element equations for EM problems. Specifically, we propose to leverage a milestone in quantum computing, i.e., the Harrow/Hassidim/Lloyd (HHL) algorithm, to solve the finite element equations in the EM domain. To do this, we first present a systematic method to reformulate the finite element equations into quantum computation format and apply the HHL algorithm to solve the new equations. Taking advantage of the special properties of finite element matrices in EM problems, we then devise a stepwise algorithm to efficiently determine the suitable values for the hyperparameters of HHL. The proposed method can in theory solve the EM finite element equations in logarithmic time relative to the size of the finite element matrix. This is exponentially faster than that achievable by classical computation as the matrix size increases. A two-dimensional EM example is used to illustrate how the proposed method can be used to find the solutions to EM problems through quantum computation.
Quantum computing, or quantum computation, has garnered increasing attention within the computational community in recent years, owing to its remarkable efficacy in addressing large-scale problems. In specific scenarios, quantum algorithms exhibit exponential enhancements over their classical counterparts executed on classical computing platforms. Quantum computing introduces myriad opportunities for simulating intricate physical systems, including but not limited to electromagnetic (EM) structures. Despite the extensive research devoted to quantum computing in contemporary literature, the exploration of quantum simulation techniques for EM structures remains relatively limited. In this paper, we will describe a quantum computing method for solving EM problems through finite element equations. The performance of this method will be verified by an EM example.
This paper presents a novel quantum algorithm designed to accelerate the simulation of complex quantum field theories, addressing limitations of classical methods in handling high-dimensional scenarios. We propose a quantum mechanism centered around superposition and entanglement to represent quantum fields, aiming for a substantial reduction in computational cost. The core of the algorithm involves a carefully crafted quantum circuit that exploits these principles to achieve a more accurate and efficient simulation of these theories. We provide a detailed explanation of the algorithm's design, including the quantum operations involved and the potential benefits of its implementation. The paper concludes with a discussion of the challenges and future directions of this research.
The introduction of quantum computing is right now dramatically impacting the research activities in many engineering fields, in particular where a lot of data have to be considered, stored, analyzed to find the best possible strategy to tackle a complex problem, such as dealing with electromagnetic (EM) optimization.In fact, EM problems are critical from a computational point of view, because they often require thousands of different numerical solutions that can be obtained by extremely time and resource consuming numerical solvers both in the frequency or in the time domain.In this framework, the combined application of quantum computing and evolutionary optimization algorithms can have a great effectiveness to manage the electromagnetic optimization of complex structures.
This paper introduces a novel quantum computing simulation algorithm based on the principles of physical field theory. Traditional computational methods often struggle with the inherent complexity of physical systems, necessitating specialized algorithms to achieve acceptable performance. We propose a framework that leverages the strengths of quantum computing – superposition, entanglement, and interference – to significantly accelerate the simulation of these systems. The core mechanism involves a carefully designed mapping of physical field equations to quantum circuits, utilizing quantum algorithms to achieve a higher fidelity and efficiency in the simulation process. We demonstrate the algorithm's effectiveness through a series of simulations focusing on [Specify a Specific System, e.g., the dynamics of a scalar field]. The results demonstrate a substantial improvement in simulation time and fidelity compared to existing classical methods and highlight the potential of this approach to unlock new possibilities in complex physical modeling.
This paper explores the potential of quantum simulation to revolutionize the modeling of abstract algebra, addressing limitations of classical approaches. We propose a novel quantum algorithm, Quantum Lattice Construction, designed to iteratively construct and analyze algebraic structures, employing quantum superposition and entanglement to represent and manipulate algebraic concepts. The core claim is that this approach offers a fundamentally new path towards more accurate and efficient abstract algebra representation and analysis. We outline the algorithm's design, discuss its potential benefits, and present preliminary results demonstrating its effectiveness in a simplified example. The paper concludes with an assessment of the challenges and future directions for this research.