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IEEE Latin American Conference on Antennas and Propagation (LACAP)

Accelerated Quantum Selection Optimization of a Microwave Filter

Gabriel F. Martinez E., Alessandro Niccolai, Eleonora Lorenza Zich, Riccardo Enrico Zich

Published Dec 1, 2024• 4 citations
Publisher

Abstract

Optimization techniques have long been employed in designing electromagnetic structures. With the emergence of quantum computing, hybrid quantum-classical optimization algorithms are appearing as promising solutions. This paper presents a new approach that integrates quantum algorithms with classical optimization to profit from the advantages of both quantum and classical computing.

DOI
10.1109/lacap63752.2024.10876263

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Optimization techniques have long been employed in designing electromagnetic structures. With the emergence of quantum computing, hybrid quantum-classical optimization algorithms are appearing as promising solutions. This paper presents a new approach that integrates quantum algorithms with classical optimization to profit from the advantages of both quantum and classical computing.
The emergence of quantum computing has brought with it multiple applications like hybrid classic-quantum evolutionary optimization algorithms. In electromagnetics, optimization has always been present to increase the performance of complex systems. This paper presents two new oracles for the genetic algorithm with quantum selection and its newer version that uses simulated binary crossover. These algorithms are applied to two common electromagnetic optimization problems showing strong results and demonstrating the flexibility that these hybrid algorithms have by making small changes to their quantum circuits.
In this thesis, we aim to answer one research question: What is the algorithmic role of classical computing in scaling up quantum computing applicability? Quantum computers have promising potential in offering more efficient solutions to some of the most demanding computational problems in the industry. There has been phenomenal progress in the theory and realization of quantum computers in the last few decades. However, quantum computing is a nascent field of technology and scaling up its computing capacity remains impeded by many open engineering/scientific challenges. One emerging solution to alleviate some of the present limitations is finding clever use of classical pre- and post-processing techniques in tandem with quantum algorithms. This helps to advantageously use scarce quantum computing resources for only a portion of a problem that benefits the most from a quantum computer. In other words, it leads to a hybrid framework in which quantum computers are considered special-purpose co-processors for accelerating specific computational tasks. We aim to develop a hybrid theoretical framework for the role of classical algorithms in scaling up quantum computing. We investigate this by focusing on three distinct limitations. We review each limitation in the context of a different application and propose an original hybrid classical-quantum solution to it. First, we focus on classical algorithms' role in a more resource-efficient compilation and embedding of quantum programs. We propose a classical algorithm that helps to embed larger optimization problems in a quantum annealer. Then we explore how a problem decomposition technique and preprocessing of the input problem can help solve significantly larger optimization problems on a quantum computer. Finally, we propose a completely hybrid quantum algorithm for preparing ground states of quantum Hamiltonians. We achieve orders of magnitude faster convergence by combining classical optimization with variational quantum state preparation.
Spurred by recent technical advances that have brought us closer to integrating many qubits, quantum technologies promise to revolutionize computation and communication. The applications that capitalize on quantum computing's superior power are vast: quantum communications (with quantum key distribution as the only protocol that guarantees unconditionally secure key growth and quantum teleportation as the driving force of the future quantum internet), quantum algorithms (providing polynomial solutions to NP problems in classical computation), quantum simulation (with accurate molecular quantum dynamics delivering a massive boost in fields such as drug design), and quantum sensors (offering support for gravimetry, seismology, and civil engineering). This talk will present the specificity of quantum computation and stress what makes it more powerful than classical computation, particularly in algorithms; at the same time, it will present the technological limitations that call for hybrid classical-quantum solutions. The primary approach in designing such hybrid algorithms is to bring a quantum optimization or speedup to a classical method. This presentation argues for a different strategy, namely the classical optimization of a quantum algorithm, and illustrates this concept with an application of Grover's algorithm.
Quantum computing and evolutionary algorithms are collaborating and combining to create a new era of computational problem-solving. Natural-selection-based evolution algorithms have demonstrated efficacy in optimizing complicated systems by iteratively improving solutions. However, quantum computing offers an entirely novel technique to handle information using superposition and entanglement, potentially accelerating optimization procedures. This study examines the theoretical underpinnings and practical implications of recent developments in quantum-enhanced evolutionary algorithms. A unique case study on 5G networks demonstrates the application of these methods to optimization problems in next-generation communication systems. We also emphasize their influence in fields where quantum-assisted evolutionary techniques spur innovation, such as financial modeling, bioinformatics, machine learning, and cryptography. Despite their revolutionary promise, several obstacles remain, such as the scalability of quantum hardware, noise sensitivity, and converting conventional evolutionary mechanisms to quantum frameworks. Developing hybrid quantum-classical and novel optimization approaches will be crucial in advancing artificial intelligence, data analytics, and decision-making systems. The author combines quantum computing with evolutionary principles to explain an advanced computational framework that has diverse scientific concepts as well as industrial applications.
Abstract: This paper explores the intersection of algorithmic quantum complexity theory and hybrid quantum-classical optimization. We propose a novel algorithm designed to efficiently find optimal configurations for complex quantum systems by strategically combining quantum computation for initial exploration and refinement with classical optimization to fine-tune the resulting quantum state. The core claim is to create a method that surpasses the limitations of classical optimization by intelligently integrating quantum and classical techniques. This work addresses the fundamental challenges in understanding and analyzing the complexity of quantum systems, potentially unlocking new algorithmic pathways and insights.
Quantum simulation has emerged as a powerful tool for optimization and exploration across diverse fields. This paper investigates the application of quantum mechanics to enhance evolutionary algorithms, proposing a novel hybrid quantum-classical simulation framework designed to accelerate the search for optimal solutions. We introduce a quantum-enhanced mutation operator, integrated with a classical optimization algorithm, to exploit quantum effects to expedite the exploration of evolutionary parameter spaces. The core mechanism focuses on utilizing quantum phenomena – specifically superposition and entanglement – to improve the representation and manipulation of evolutionary trajectories, ultimately leading to faster convergence and the identification of optimal solutions. The research explores the potential of this approach to overcome limitations in traditional evolutionary algorithm implementation, offering a promising pathway for enhanced optimization capabilities.