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Unconstrained Quantum Genetic Algorithm for Massive MIMO System

Abdulbasit M. A. Sabaawi, Mohammed Rabeea Almasaoodi, Sara El Gaily, Sándor Imre

Published Jul 11, 2023• 7 citations
Publisher

Abstract

There are plenty of real-world applications that require finding extreme value in an unsorted database. This database can be enormously large, such that there is no available quantum computer or classical supercomputer that can execute the search process. We proposed a new unconstrained quantum genetic algorithm (QGA) in order to increase the probability of finding the global solution and escaping from local minima. This algorithm exploits the features provided by blind quantum computation (BQC), which holds the promise to handle this computation issue by delegating computation to quantum remote devices. Massive multiple-input multiple-output (MIMO) systems are used as a toy example for demonstrating the effectiveness of the developed quantum genetic method.

DOI
10.1109/contel58387.2023.10198943

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There are plenty of real-world applications that require finding extreme value in an unsorted database. This database can be enormously large, such that there is no available quantum computer or classical supercomputer that can execute the search process. We proposed a new unconstrained quantum genetic algorithm (QGA) in order to increase the probability of finding the global solution and escaping from local minima. This algorithm exploits the features provided by blind quantum computation (BQC), which holds the promise to handle this computation issue by delegating computation to quantum remote devices. Massive multiple-input multiple-output (MIMO) systems are used as a toy example for demonstrating the effectiveness of the developed quantum genetic method.
In the past decades, many quantum algorithms have been developed. The main obstacle that prevents the widespread implementation of these algorithms is the small size of the available quantum computer in terms of qubits. Blind Quantum Computation (BQC) holds the promise of handling this issue by delegating computation to quantum remote devices. Here, we introduce a novel Constrained Quantum Genetic Algorithm (CQGA) that selects the optimum extreme (minimum or maximum) value of a constrained goal function (or a vast unsorted database) with very low computational complexity. Since the convergence speed to the optimal solution for the Constrained Classical Genetic Algorithm (CCGA) is highly dependent on the level of quality of the initially selected potential solutions, the CCGA's heuristic initialization stage is replaced by a quantum one. This is achieved by exploiting the strengths of the Constrained Quantum Optimization Algorithm (CQOA) and the BQC. The proposed CQGA is applied as an embedded computational infrastructure for the uplink multi-cell massive MIMO system. The algorithm maximizes the energy efficiency (EE) of the uplink massive MIMO while considering different users target bit rate classes. Simulation results show that the suggested CQGA maximizes energy efficiency through careful computation of the optimal transmit power for each active user using fewer computational steps than the CCGA. We demonstrated that when the overall transmit power set or the overall number of active users increases, the CQGA keeps executing a smaller number of generation steps compared to the CCGA. For instance, if we consider a scenario where the overall number of active users () is set to 18, the CQGA finds the optimal solution with a smaller number of generation steps equal to 6, while the CCGA takes a larger number of generation steps, reaching 65.
Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.
Abstract Devising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.
Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.
Quantum genetic algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.
This paper presents a new methodology for running Genetic Algorithms on a Quantum Computer. To the best of our knowledge and according to reference [6]there are no feasible solutions for the implementation of the Quantum Genetic Algorithms (QGAs). We present a new perspective on how to build the corresponding QGA architecture. It turns out that the genetic strategy is not particularly helpful in our quantum computation approach; therefore our solution consists of designing a special-purpose oracle that will work with a modified version of an already known algorithm (maximum finding [1]), in order to reduce the QGAs to a Grover search. Quantum computation offers incentives for this approach, due to the fact that the qubit representation of the chromosome can encode the entire population as a superposition of basis-state values.