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IEEE Congress on Evolutionary Computation (CEC)

On the Effect of Quantum Noise in Quantum Genetic Algorithms

Giovanni Acampora, Roberto Schiattarella

Published Jul 1, 2023• 4 citations
Publisher

Abstract

In recent years, quantum computing is finding strong application in the development of new evolutionary algorithms. This is mainly due to the intrinsic parallelism induced by this new computational paradigm, which fits well with the structure of population-based optimization algorithms. However, the pioneering nature of quantum hardware makes quantum computations still suffer from noise that affects their accuracy and precision. This study aims to analyze the effect of this noise on quantum genetic algorithms and to quantify how much this can destroy or enhance the guided search of these algorithms in the search space. In detail, the proposed paper introduces a systematic study that assess the effects of quantum noise in a genetic algorithm equipped with a quantum recombination operator, formally known as the Quantum Mating Operator, when applied to solving a well-known optimization problem such as the 01 knapsack.

DOI
10.1109/cec53210.2023.10254078

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In recent years, quantum computing is finding strong application in the development of new evolutionary algorithms. This is mainly due to the intrinsic parallelism induced by this new computational paradigm, which fits well with the structure of population-based optimization algorithms. However, the pioneering nature of quantum hardware makes quantum computations still suffer from noise that affects their accuracy and precision. This study aims to analyze the effect of this noise on quantum genetic algorithms and to quantify how much this can destroy or enhance the guided search of these algorithms in the search space. In detail, the proposed paper introduces a systematic study that assess the effects of quantum noise in a genetic algorithm equipped with a quantum recombination operator, formally known as the Quantum Mating Operator, when applied to solving a well-known optimization problem such as the 01 knapsack.
Quantum Genetic Algorithms (QGAs) are emerging as a new class of algorithms that exploit the peculiarities of quantum computers to outperform classical evolutionary approaches in solving complex optimization problems. Despite the theoretical promises, their applicability has been strongly affected by the limitation of current noise-prone quantum hardware. It has only recently been demonstrated that acceptable levels of quantum noise can enhance the exploration capability of QGA in constrained optimization problems. The research corroborates the initial findings, providing further evidence of the efficacy of these algorithms by successfully resolving the NP-hard Ising Problem through the utilization of a QGA equipped with a quantum mating operator. The experimental results show that noisy simulations of QGA are capable of identifying better solutions than genetic algorithms that are equipped with state-of-the-art classical mating operators.
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.
Recently, quantum computing has emerged as a new paradigm that promises to improve artificial intelligence techniques. One of the research fields that is certainly benefiting from this new computational paradigm is evolutionary optimization. In literature, efforts have been already made to run evolutionary algorithms on quantum computers using quantum effects such as superposition and entanglement to converge towards sub-optimal solutions of hard problems. However, the performance of these quantum evolutionary approaches is limited by the number of qubits available on current quantum devices. This limitation is more noticeable in the case of continuous optimization problems, where the search space is potentially infinite. This paper presents a recursive algorithmic structure that embodies a quantum evolutionary algorithm to overcome the limitations mentioned above. The result is an innovative and efficient approach in the context of quantum evolutionary optimization.
Quantum computing is a promising and emerging field of computer science with the potential to improve artificial intelligence techniques, particularly those related to optimization. However, current quantum devices operate in the Noisy Intermediate-Scale Quantum era, in which quantum noise can significantly compromise computational reliability. Evolutionary algorithms are inherently stochastic and may therefore be less sensitive to such noise, or even benefit from it. This study evaluates the performance of a hybrid quantum genetic algorithm on a set of combinatorial and continuous optimization problems under various realistic noise models. The findings demonstrate that quantum noise does not hinder the algorithm's performance and can even enhance its exploratory capabilities in some cases. This suggests that noise-aware quantum evolutionary strategies have the potential to be effective even with today's imperfect quantum hardware.
Abstract Quantum computers promise to revolutionize the world of computing thanks to some features of quantum mechanics that can enable massive parallelism in computation. This benefit may be particularly relevant in the design of evolutionary algorithms, where the quantum paradigm could support the exploration of multiple regions of the search space in a concurrent way. Although some efforts in this research field are ongoing, the potential of quantum computing is not yet fully expressed due to the limited number of qubits of current quantum processors. This limitation is even more acute when one wants to deal with continuous optimization problems, where the search space is potentially infinite. The goal of this paper is to address this limitation by introducing a hybrid and granular approach to quantum algorithm design, specifically designed for genetic optimization. This approach is defined as hybrid, because it uses a digital computer to evaluate fitness functions, and a quantum processor to evolve the genetic population; moreover, it uses granular computing to hierarchically reduce the size of the search space of a problem, so that good near-optimal solutions can be identified even on small quantum computers. As shown in the experiments, where IBM Q family processors are used, the usage of a granular computation scheme statistically enhances the performance of the state-of-the-art evolutionary algorithm implemented on quantum computers, when it is run to optimize well-known benchmark continuous functions.
By violating the extended Church-Turing thesis, the quantum computing (QC) paradigm has emerged as a serious contender to classical computing. We now know that certain classical exponential problems (NP (nondeterministic polynomial time)-complete) can be solved in polynomial time by QC. Shor’s integer factorization and Grover’s method for unstructured search are perhaps the most well-known quantum algorithms. QC has been successfully applied to a variety of domains including optimization problems. Optimization can be solved by using deterministic/numerical as well as stochastic methods. Genetic algorithms (GA) belong to the latter category. Incorporating quantum mechanical properties such as superposition, entanglement, interference into the structure of GA has resulted in a more efficient evolutionary computing method called the quantum-inspired genetic algorithm. This method has been successfully applied to image processing, signal processing, economic load dispatch, coalition formation, task scheduling, etc. This chapter presents an overview of the current trends and developments in the domain of quantum-inspired genetic algorithms with the aim of investigating areas of further research in this sphere.