• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.6
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• pp.590-599
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• 2024

Quantum computing is set to transform the field of materials science, offering computational methods that could far surpass conventional approaches for tackling intricate material design challenges. This review introduces the foundational principles of rapidly growing quantum computing and its application trends in the design and analysis of nanomaterials. We explain how quantum speedup, achieved through quantum algorithms utilizing qubit superposition and entanglement, is applied to material design. Additionally, the principles and research trends of quantum variational methods, including the Variational Quantum Eigensolver (VQE), which has recently gained attention as a quantum algorithm simulation technique, will be discussed. By combining new techniques based on quantum algorithms with the quantum speed-up, the quantum computing is expected to offer new insights into data-intensive materials research and provide innovative methodologies for the development of new functional materials. With the advancement of quantum algorithms, the field of materials science could enter a new era, enabling more precise and efficient approaches in materials design and functional analysis.

• ETRI Journal
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• v.46 no.5
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• pp.793-805
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• 2024

Entanglement is crucial for achieving quantum advantages. However, in the context of quantum machine learning, existing optimization strategies for generating quantum classifier circuits often result in unentangled circuits, indicating an underutilization of the entanglement effect needed to learn complex patterns. In this study, we proposed a novel metaheuristic approach-genetic algorithm-for designing a quantum kernel classifier that incorporates expressive entanglement. This classifier utilizes a loopless entanglement-directed graph, where each directed edge represents the entanglement between the target and control qubits. The proposed method consistently outperforms classical and quantum baselines across various artificial and actual datasets, achieving improvements up to 32.4% and 17.5%, respectively, compared with the best model among all other baselines. Moreover, this method successfully reconstructs the hidden entanglement structures underlying artificial datasets. The results also demonstrate that the optimized circuits exhibit diverse entanglement variations across different datasets, indicating the versatility of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1149-1156
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• 2021

The Grover algorithm, which accelerates the brute force attack, is applicable to key recovery of symmetric key cryptography, and NIST uses the Grover attack cost for symmetric key cryptography to estimate the post-quantum security strength. In this paper, we estimate the attack cost of Grover's algorithm by implementing DES as a quantum circuit. NIST estimates the post-quantum security strength based on the attack cost of AES for symmetric key cryptography using 128, 192, and 256-bit keys. The estimated attack cost for DES can be analyzed to see how resistant DES is to attacks from quantum computers. Currently, since there is no post-quantum security index for symmetric key ciphers using 64-bit keys, the Grover attack cost for DES using 64-bit keys estimated in this paper can be used as a standard. ProjectQ, a quantum programming tool, was used to analyze the suitability and attack cost of the quantum circuit implementation of the proposed DES.

• Journal of the Korean Mathematical Society
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• v.45 no.4
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• pp.1135-1202
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• 2008

We construct the Fock space representations of classical quantum affine algebras using combinatorics of Young walls. We also show that the crystal graphs of the Fock space representations can be realized as the crystal consisting of proper Young walls. Finally, we give a generalized version of Lascoux-Leclerc-Thibon algorithm for computing the global bases of the basic representations of classical quantum affine algebras.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• ETRI Journal
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• v.43 no.3
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• pp.483-510
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• 2021

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in O (N) time, whereas quantum algorithm design is based on Grover's method, which completes the search in $O(\sqrt{N})$ time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are $O(\sqrt{t})$ and $O(\sqrt{N})$, and the exceptional worst case is bounded by O (t) and O (N). Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3606-3629
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• 2018

The discovered high potential utility itemsets (HPUIs) have significant influence on a variety of areas, such as retail marketing, web click analysis, and biological gene analysis. Thus, in this paper, we propose an algorithm called HPUIM-QGA (Mining high potential utility itemsets based on a quantum genetic algorithm) to mine HPUIs over uncertain datasets based on a quantum genetic algorithm (QGA). The proposed algorithm not only can handle the problem of the non-downward closure property by developing an upper bound of the potential utility (UBPU) (which prunes the unpromising itemsets in the early stage) but can also handle the problem of combinatorial explosion by introducing a QGA, which finds optimal solutions quickly and needs to set only very few parameters. Furthermore, a pruning strategy has been designed to avoid the meaningless and redundant itemsets that are generated in the evolution process of the QGA. As proof of the HPUIM-QGA, a substantial number of experiments are performed on the runtime, memory usage, analysis of the discovered itemsets and the convergence on real-life and synthetic datasets. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful HPUIs from uncertain datasets.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.941-950
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• 2017

The round robin algorithm is regarded as one of the most efficient and effective CPU scheduling techniques in computing. It centres on the processing time required for a CPU to execute available jobs. Although there are other CPU scheduling algorithms based on processing time which use different criteria, the round robin algorithm has gained much popularity due to its optimal time-shared environment. The effectiveness of this algorithm depends strongly on the choice of time quantum. This paper presents a new effective round robin CPU scheduling algorithm. The effectiveness here lies in the fact that the proposed algorithm depends on a dynamically allocated time quantum in each round. Its performance is compared with both traditional and enhanced round robin algorithms, and the findings demonstrate an improved performance in terms of average waiting time, average turnaround time and context switching.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.