Weightless neural network parameters and architecture selection in a quantum computer

Silva, Adenilton J. da; Oliveira, W.; Ludermir, Teresa B.

doi:10.1016/j.neucom.2015.05.139

Cited by 17 publications

(5 citation statements)

References 40 publications

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“…QWLNN [59] mentions QWLNN in 2008. [60] defines a QWLNN architecture learning algorithm based on quantum superposition. The architecture and parameters of this model depend on many factors such as the number of training modes, the structure of the selector, etc.…”

Section: H Othersmentioning

confidence: 99%

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A review of Quantum Neural Networks: Methods, Models, Dilemma

Zhao,

Wang

2021

Preprint

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The rapid development of quantum computer hardware has laid the hardware foundation for the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and computational efficiency compared to its classical counterparts. This article will review the development of QNN in the past six years from three parts: implementation methods, quantum circuit models, and difficulties faced. Among them, the first part, the implementation method, mainly refers to some underlying algorithms and theoretical frameworks for constructing QNN models, such as VQA. The second part introduces several quantum circuit models of QNN, including QBM, QCVNN and so on. The third part describes some of the main difficult problems currently encountered. In short, this field is still in the exploratory stage, full of magic and practical significance.

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Section: H Othersmentioning

confidence: 99%

“…In the recent quantum processor stage, some emerging models such as QBM [29]- [31], QCVNN [32]- [34], QGAN [35] [36], QGNN [37]- [39], QRNN [40] [41], QTNN [42], QP [43]- [49], etc. [50]- [60] will be introduced in subsequent sections.…”

Section: Introductionmentioning

confidence: 99%

A review of Quantum Neural Networks: Methods, Models, Dilemma

Zhao,

Wang

2021

Preprint

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“…Training WNNs involves a different approach compared to traditional neural networks. WNNs focus on the activation of concepts rather than the activation of individual neurons [14]. This means that instead of adjusting the weights of connections between neurons during training, the weights of concepts and their associations are adjusted.…”

Section: Introductionmentioning

confidence: 99%

Weightless neural network-based fault diagnosis in suspension system

Shah,

Venkatesh,

Balaji

et al. 2024

FME Transactions

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Vehicle suspension systems play a critical role in ensuring passenger comfort and safety. Detecting faults in these systems is vital for maintaining safety, performance, and cost-effectiveness. Traditional inspection methods have limitations, such as visual checks, bounce tests, and alignment assessments. This study explores Wilkie, Stonham, and Aleksander Recognition Device (WiSARD), a weightless neural network (WNN), for suspension fault diagnosis. A WNN model is employed to classify suspension system faults using sensor data. The dataset includes both normal and faulty conditions to train the model. The study assesses WiSARD under various fault conditions, including strut damage, mount failure, worn-out components, and low wheel pressure. Comparative evaluations demonstrate that the approach outperforms other classification techniques, achieving an impressive 95.63% accuracy with a rapid 0.05-second computation time for test data. This WNN-based method proves superior in detecting suspension faults and holds potential as a candidate for real-time vehicle fault diagnosis systems.

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“…Better understanding the many classes of quantum algorithms and their behaviors might be the first step toward an approach to resolve huge computational problems in emerging computing areas, such as artificial intelligence and robotics, big data analysis, bioinformatics, and cyber security [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

HybriD-GM: A Framework for Quantum Computing Simulation Targeted to Hybrid Parallel Architectures

Ávila

Santos

Cruz

et al. 2023

Entropy

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This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.

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Weightless neural network parameters and architecture selection in a quantum computer

Cited by 17 publications

References 40 publications

A review of Quantum Neural Networks: Methods, Models, Dilemma

A review of Quantum Neural Networks: Methods, Models, Dilemma

Weightless neural network-based fault diagnosis in suspension system

HybriD-GM: A Framework for Quantum Computing Simulation Targeted to Hybrid Parallel Architectures

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