The emergence of quantum annealers has catalyzed the development of quantum optimization algorithms across various fields. The formulation of a quadratic unconstrained binary optimization (QUBO) model for quantum line...
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ISBN:
(纸本)9798331541378
The emergence of quantum annealers has catalyzed the development of quantum optimization algorithms across various fields. The formulation of a quadratic unconstrained binary optimization (QUBO) model for quantum linear systems suggests that quantum optimization algorithms can play an important role in science and engineering. Recently, a QUBO model was developed by applying a quantum linear system to computed tomography (CT) image reconstruction to clearly show the internal structure of the sample. To date, ultra-clear CT images have not been reconstructed using quantum optimization algorithms owing to limitations in logical qubits. However, the quantum CT image reconstruction algorithm using a quantum linear system provides confidence in the accuracy of the CT image by leveraging the difference between the global and theoretical minimum energies. In this paper, we propose an algorithm that can use two or more QUBO models for CT image reconstruction. The proposed algorithm formulates several QUBO models from CT image reconstruction and verifies them using the hybrid solver of the D-Wave system. The proposed algorithm suggests that quantum-optimized CT algorithms can make greater progress in the future.
This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay c...
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This study explored the challenge of multi-equipment collaborative scheduling and management within container terminals, focusing on essential equipment such as reach stackers, automated guided vehicles (AGVs), quay cranes, and yard cranes. The job shop scheduling problem (JSSP) in container handling was modeled using a quadratic unconstrained binary optimization (QUBO) formulation. A novel scheduling approach is developed based on the synergistic combination of an artificial intelligence (AI) algorithm and quantum computing (QC) (or AI-QC scheduler). This strategy integrates deep reinforcement learning (DRL) and a quantum approximate optimizationalgorithm (QAOA) to minimize the makespan (total completion time) with accurate coordination of port equipment. Based on operating scenarios in port environments, the proposed AI-QC scheduler offers significant advantages over traditional methods such as first-in-first-out (FIFO), shortest processing time (SPT), and genetic algorithms (GA), particularly in handling stochastic disturbances in processing times. The hybrid technique based on deep learning, quantum computing, and (DRL-QAOA) has self-optimization capabilities, adjusting to changes in market demand in port operations. The AI-QC scheduler can outperform benchmark algorithms in allocating and scheduling port resources, achieving a lower makespan and enhanced operational efficiency across various scenarios. Robust performance indicates optimizing equipment utilization and enhancing terminal throughput, particularly in unpredictable scenarios. Hence, deep-learning AI-driven quantumoptimization strategies can improve the competitiveness and efficiency of container terminal operations in a rapidly evolving global market.
Rising market demands, economic pressures, and technological advancements have spurred researchers to seek ways to enhance business environments and scientific productivity. Predictive science, crucial in this context...
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Rising market demands, economic pressures, and technological advancements have spurred researchers to seek ways to enhance business environments and scientific productivity. Predictive science, crucial in this context, has gained prominence due to the rapid progress in information technology and forecasting algorithms. Time series forecasting, widely used in fields like engineering, economics, tourism, and energy, has inherent limitations with classical statistical methods, leading researchers to explore artificial intelligence and fuzzy logic for more accurate predictions. However, despite extensive efforts to improve accuracy, challenges persist. The research introduces a model aimed at surpassing existing methods in time series forecasting accuracy. This approach combines meta-heuristic optimizationalgorithms and neutrosophic logic to enhance precision in uncertain and complex environments, promising improved forecasting outcomes. The study shows that the performance of the neutrosophic time series modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. This study selects the quantum optimization algorithm (QOA), genetic algorithm (GA), and particle swarm optimization (PSO) to address this weakness. These optimizationalgorithms improve the performance of the NTS modeling approach by selecting the global universe of discourse and corresponding intervals from the list of locally optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index, and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signified the efficiency of the proposed model over existing benchmark models in terms of average forecasting error rate (AFER). This value using the proposed NTS QOA, NTS GA, and NTS PSO method on the university dataset is 0.166, 0.167, 0.164, o
Railways are popular for moving goods because they are fast and can carry a lot. Due to technological development, researchers are now thinking about using self-driving systems instead of regular trains to make transp...
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ISBN:
(纸本)9798331540661;9798331540678
Railways are popular for moving goods because they are fast and can carry a lot. Due to technological development, researchers are now thinking about using self-driving systems instead of regular trains to make transporting goods more efficient and safer. Current train control systems sometimes make slow decisions which can be unsafe. This research introduces a new way to make these decisions faster and better for self-driving trains. We propose an algorithm is called the Near-field scene quantumoptimization (NFQO) algorithm. This uses live satellite images to help the train understand its surroundings. Additionally, a Hexagonal Grid (HG) tool, it helps the train pick the best route quickly. The main advantage of developing quantumalgorithm is used to make decisions super-fast and accurate. When combined NFQO and HG with satellite images, improves reliable and effective self-driving cargo trains transportation. We've tested NFQO, and it's better than other available tools. This research, which combines quantum technology, satellite images, and self-driving systems, points to exciting future developments in train transportation.
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