Earticle

Since the emergence of quantum computing, Grover’s algorithm has been widely studied as a method to attack Proof-of-Work (PoW) blockchains. However, it requires multiple oracle and diffuser iterations, causing high computational overhead and ancilla qubit usage. In this paper, we propose a novel quantum algorithm that efficiently finds valid nonces in PoW-based blockchains without iterative amplification. It introduces a non-unitary quantum operator that directly generates a superposition of valid nonces in one step. We also present an efficient quantum circuit implementation and theoretically prove that our algorithm significantly reduces computational resources compared to conventional Grover-based methods.

Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．

An improved algorithm of quantum particle swarm with chaos is presented to solve the problem that the traditional particle swarm algorithm is easy to fall into local optimum and converges very slowly. Through analysis the current state of particles in the iteration, to determine and deal with the particle of poor performance, and keep the normal state continue to complete the search optimal solution, which effectively inhibit premature phenomenon of the particles, and improve the overall search ability of particle swarm. At the same time, in order to improve the performance of the algorithm, introducing chaos mechanism, further enhance the search ability of particle. The experiments of benchmark function show that the improved algorithm has obvious advantages compared with the other two algorithms, it has higher stability and accuracy and faster convergence speed at the same time.

Particle swarm optimization (PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. It is a simple and efficient optimization algorithm. But it exists the low computational speed and easy falling into local optimal solution in solving the complex problem. So the quantum theory, adaptive inertia weight, disturbance factor and diversity mutation strategy are introduced into the PSO algorithm in order to propose an improved PSO(IWDMDQPSO) algorithm in this paper. In the IWDMDQPSO algorithm, the quantum theory is used to change the updating mode of the particles for guaranteeing the simplification and effectiveness of the algorithm. The adaptive inertia weight is used to improve the premature convergence of the algorithm. The disturbance factor is used to avoid the premature of the algorithm. The diversity mutation strategy is used to improve the global searching ability and computation speed. Finally, the famous benchmark functions are selected to prove the performance and effectiveness of the proposed IWDMDQPSO algorithm. The experiment results show that the proposed IWDMDQPSO algorithm takes on better solving accuracy and higher computation speed in solving the complex function. So it has a remarkable optimization performance.

A fuzzy energy management strategy was designed for the single-axle parallel hybrid electric vehicles, and then the quantitative factor of the fuzzy logic controller was optimized by the quantum PSO algorithm under the Matlab platform based on the equivalent fuel economy. Then a comparison test about the energy management before and after the optimization was carried out based on the secondary development of Advisor which proves that the energy management strategy optimized by the quantum PSO algorithm can improve the SOC of the battery pack by 18% when the VAIL2NREL cycle finished while the power of the vehicles nearly remain the same. What’s more, the optimized strategy can make the engine and the motor works in the high efficient area for most of the time which can improve the recycling rate of the energy and reduce the equivalent fuel consumption effectively.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicabilities, and the predicting results has bigger volatility and lower accuracy. In view of the above problems, a method based on least square support vector regression (LSTSVR) and real-coded quantum evolutionary algorithm (RQEA) is proposed. Firstly, the LSTSVR parameter is chosen as a combinatorial optimization problem, and determining the objective function of the combinatorial optimization problem, then, using RQEA to solve the combinatorial optimization problem and optimize the LSTSVR parameters, Finaly, LSTSVR-RQEA is used to sovle the prediction of subgrade settlement. The simulation results show that RQEA is an effective method to select LSTSVR parameters, and has excellent performance when applied to the prediction of subgrade settlement.

Due to the normal forecasting methods for subgrade settlement using observation data have different explicabilities, and the predicting results has bigger volatility and lower accuracy. The Combined forecasting model of subgrade settlement based on forecasting availability and real-coded quantum evolutionary algorithm (RQEA) is put forward in this paper. At the first, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve with the S-type characteristics are chosen as single forecasting model; Then, to get the weights of each single forecasting model, objective function is build on the basis of standard of forecasting availability maximization, and RQEA is employed to solve the objective function, and to construct the combined forecasting model of subgrade settlement. The result of engineering practice shows that the proposed method has better prediction accuracy and stability, and can meet engineering demand.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicability and disadvantages, The Combined forecasting model is put forward based on support vector machine (SVM) and real-coded quantum evolutionary algorithm (RQEA) in this paper. Its core is that, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve which has S-type characteristic are chosen as single forecasting model, then support vector machine is used to combine the predicting results of each single forecasting model, at the same time, RQEA is adopted to optimize support vector machine parameter to improve the SVM’s performance. The analytical result of engineering practice indicates that the proposed combined forecasting model of subgrade settlement base on SVM and RQEA can not only improve the predicting accuracy, but also reduce the predicting risk, and can meet engineering demand.

Co-evolutionary mechanism is now used into evolutionary algorithms and provides these algorithms the power to promote the convergence. In order to promote the performance of the traditional quantum-inspired evolutionary algorithm (QEA), we proposed a novel quantum-inspired co-evolutionary algorithm (NQCEA), in this paper. The quantum state population is firstly divided into multiple sub-populations, which complete the evolution processes independently. In each evolution cycle, every sub-population will produce an elitist individual, which is then selected to construct an elite library. Subsequently, these individuals in this elite library can help the poor sub-population to find the global optimal solution or near-optimal solution. In addition, a diversity indicator is defined for every sub-population and is used to measure the diversity of the corresponding sub-population. As for the sub-population with poor diversity, the mutation strategies are implemented in order to expand its diversity and improve its global search ability. Finally, the NQCEA is compared with the traditional QEA to test their performance. Experiments are performed on the global numerical optimization functions and the simulation results indicate that this new algorithm has the characteristics of good global search capability and more stable performance than the traditional QEA.

The standard Glowworm Swarm Optimization(GSO) has poor global search ability and easily trap into local optimum. In order to solve these problems, a Quantum Glowworm Swarm Optimization Algorithm based on Chaotic Sequence(QCSGSO) is proposed in this paper.Firstly, chaotic sequence is generated to initialize the population, which has higher probability to cover more local optimal areas, and provides a good condition for further optimization and tuning.Then, quantum behavior is applied to elite population, which makes individuals locate in any position of the solution space randomly with a certain probability, greatly enhances the algorithm’s capability of global searching and local optimum jumping. Finally, QCSGSO adopts single dimension loop swimming rather than the original fixed step movement mode, which not only improves the solution precision and convergence speed, but also solves GSO’s problem about too sensitive to the step-size, and enhances the robustness of the algorithm indirectly. The results of simulation experiments show that the proposed method is feasible and effective.

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

This paper proposes the application method of an Adaptive Quantum-Inspired Evolutionary Algorithm (AQEA) to Vehicular Ad Hoc Networks (VANETs) for enhancing clustering and routing performance. AQEA integrates quantum-inspired principles, including quantum bits, quantum superposition, and adaptive quantum rotation gates, to effectively navigate the highly dynamic and complex environments characteristic of VANETs. By dynamically balancing exploration and exploitation, AQEA encodes cluster configurations as quantum states and adjusts them using a fitness-driven rotation operator. Comparative simulations reveal that AQEA consistently produces larger, more stable clusters and reduces both reconfiguration overhead and routing costs compared to conventional algorithms such as the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA). AQEA consistently achieves larger and more stable clusters, significantly reduces cluster reconfiguration overhead, and minimizes routing costs. Statistically significant improvements were observed: a 59.5% increase in cluster size and a 29.10% reduction in stability penalty relative to WOA, and a 32.99% reduction in routing cost compared to GOA. These results confirm AQEA’s superior adaptability and robustness, positioning it as an effective solution for managing clustering and routing in dynamic VANET environments. These results validate the practical relevance and algorithmic superiority of AQEA, positioning it as a robust and adaptive solution for managing clustering and routing in dynamic VANET scenarios. Also, these results highlight AQEA’s robustness and adaptability, positioning it as an effective solution for managing clustering and routing in dynamic VANET scenarios. Future research directions include real-world validations, expanded performance evaluations, and further refinement of the algorithm's adaptive mechanisms.

A novel cultural quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to improve the performance of the quantum-behaved PSO (QPSO). The cultural framework is embedded in the QPSO, and the knowledge stored in the belief space can guide the evolution of the QPSO. 15 high-dimensional and multi-modal functions are employed to investigate the proposed algorithm. Numerical simulation results demonstrate that the CQPSO can indeed outperform the QPSO.

Prediction of network public opinion is a complicated prediction featuring poor information, small samples and uncertainty. A prediction model of network public opinion based on grey support vector machine (SVM) is specified to increase prediction accuracy. First, network data are preprocessed by text clustering, hotpot extraction and data aggregation. Then a time series model GM(1,1) is established and SVM is used to modify prediction outcomes of GM(1,1). At last, simulation experiment is conducted to test performance of the model. Simulation results indicate that grey SVM improves the prediction accuracy of network public opinion compared with traditional prediction models. The predictions have certain practical values.

Due to the low computational precision, local optimal solution and slow convergence speed of particle swarm optimization (PSO) algorithm, an improved PSO (SAQPSO) algorithm based on simulated annealing (SA) and quantum theory is proposed in this paper. The first, quantum theory is used to change the updating mode of the particles in order to improve the search speed and the convergence precision, and guarantee the simplification and effectiveness. Then the SA with probability and local search ability is introduced into quantum PSO (QPSO) in order to keep the diversity of the population, avoid falling into local optimum and enhance the global search ability. The SAQPSO algorithm keeps the characteristics of the simple and easy implementation, improves the global optimization ability and the convergence speed and the accuracy. Finally, some benchmark functions are used to prove the validity of the proposed SAQPSO algorithm. The computational results show that the proposed SAQPSO algorithm takes on the fast convergence speed, the better robustness and global search ability.

This paper introduces an algorithm of wavelet biological image denoising integrated into quantum-inspired method, which is based on the basic principles of quantum mechanics. The quantum-inspired method is applied to estimate the respective probability of wavelet coefficients of the noise and signal of the biological image, then dynamically self-adapt to estimate the semi-soft threshold of wavelet coefficients, so as to protect the signal wavelet coefficients and filter the noise wavelet coefficient. Therefore, the proposed algorithm can restore the noise biological image and protect details of the biological image. Experimental results show that the proposed algorithm can improve the image quality and it is better than other denoising algorithms.

This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetism-like mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

Quantum-Inspired Evolutionary Algorithm(QEA)은 알고리즘 자체에 충분한 data-level parallelism이 내재되어 있어 GPU를 이용한 가속에 용이하다. 그러나 효과적인 실행시간의 단축을 위해서는 CPU와 GPU에의 적절한 task-mapping이 필요하다. 이때 단순히 함수 자체의 병렬성만을 고려하는 것이 아니라 CPU와 GPU간의 데이터 전송도 고려하여 task-mapping을 할 필요가 있다. 또한 추가적인 성능향상을 위하여 zero-copy host memory와 적절한 execution configuration의 사용, 그리고 memory coalescing 등을 이용할 수 있다. 그 결과 30,000개의 item수를 가진 0-1 knapsack problem에 대한 QEA의 수행을 multi-threading CPU에 비해 평균 3.69배 빠르게 할 수 있었다.

Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.

특징 선택은 패턴 인식의 성능을 향상시키기 위해 부분집합을 구성하는 중요한 문제다. 특징 선택에는 순차 탐색 알고리즘으로부터 확률 기반의 유전 알고리즘까지 다양한 접근 방법이 적용 되었다. 본 연구에서는 특징 선택을 위해 양자 비트, 상태의 중첩 등 양자 컴퓨터 개념을 기반으로 하는 양자 기반 유전 알고리즘(QGA: Quantum-inspired Genetic Algorithm)을 적용하였다. QGA 성능은 전통적인 유전 알고리즘(CGA: Conventional Genetic Algorithm)을 적용한 특징 선택 방법과 분류율 및 평균 특징 개수의 비교를 통해 이루어졌으며, UCI 데이터를 이용한 실험 결과 QGA를 적용한 특징 선택 방법이 CGA를 적용한 경우에 비해 전반적으로 좋은 성능을 보임을 확인 할 수 있었다.

Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.