Architectures of Quantum Memory-driven Computing

Hahanov, Vladimir; Chumachenko, Svetlana; Litvinova, Eugenia; Khakhanova, Hanna

doi:10.1109/ewdts.2018.8524843

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…e qubits (atomic ions) are trapped and designed by groupings of static and oscillating electric fields [33,36,37]. In what way, these quantum data are stored in these repositories which are beyond the scope of this research.…”

Section: Quantum Phenomenonmentioning

confidence: 99%

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Hasan

Ahmad

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.

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Section: Quantum Phenomenonmentioning

confidence: 99%

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Hasan

Ahmad

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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“…У роботах [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] інтегрально реалізовані кубітні моделі, квантові методи та комбінаторні алгоритми технічного діагностування цифрових пристроїв. Робота [10] присвячена проблемам оперативного тестування обчислювальних схем, що оперують наближеними даними.…”

Section: огляд літературиunclassified

“…Інтегрально, реалізовані кубітні моделі, квантові методи та комбінаторні алгоритми технічного діагностування цифрових пристроїв [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], які дають можливість суттєво (до 25 %) зменшити час синтезу тестів, дедуктивного моделювання несправностей та справної поведінки, пошуку дефектних станів за рахунок впровадження інноваційної ідеї використання кубітно-векторних структур даних для опису логічних компонентів (рис. 23).…”

Section: обговоренняunclassified

Quantum Digital-Analogue Computing

Khakhanova¹,

Chumachenko²,

Rakhlis³

et al. 2022

RIC

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Context. Nature is the relation among processes and phenomena. Nothing exists in the universe without relations. Computer is transactions of relations between data with the help of control and execution mechanisms. Quantum relations are a superposition of particles and their states. Superposition and entanglement are equivalent concepts. Entanglement is a non-local superposition of deterministic states. A quantum computer is unconditional transactions of relations between qubit data. Quantum computer is an analog device for parallel solution of combinatorial problems. Practically oriented definitions of the quantum computer concepts are the path to development of scalable quantum parallel algorithms for combinatorial problems solving. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer wants to use previously created primitive constructs to build an always non-optimal computing unit. The paradigm of quantum computer creation is determined through the use of photonic transactions on the electrons of an atom may exclude the use of quantum logic. The evolutionary path of a quantum computer from the classical one: “memory-address-transaction” (MAT) → “electron-addresstransaction” → “electron-address-quantaction” (EAQ) → state-superposition-logic. The meeting point of classical and quantum computers is photon transactions on the structure of electrons. Everything that is calculated on a quantum computer can be calculated in parallel on a classical one on account of memory redundancy. The given example is a memory-driven algorithm for modeling digital products based on qubit-vector forms of functionality description for significant performance boost of computing processes by parallel execution of logical operations. Objective. Simulation of the correct SoC-component behavior based on vector representation of the logic. Formation of the triggering development of a computing based on the superposition of the classical, quantum and analog computing process, which in its development should be based on technological qubit, tabular and vector data structures for the parallel solution of combinatorial problems. Method. MAT-computing implements any algorithms on account of transactions (read-write) in memory. Qubit-vector models for describing functionalities, which differ from known truth tables in compactness of description and manufacturability for the implementation of parallel algorithms of the synthesis and analysis of digital devices and SoC-components. Results. 1) The metric of the technological data structures, focused on parallel troubleshooting in digital systems based on the usage of two logical vector operations, was proposed for the first time. 2) The metric of relations between the individual components of QC, allowing organizing a quantum deterministic computer, has been further developed. 3) Quantum architectural solutions, that allow solving coverage problems in a quasi-parallel mode, were proposed for the first time. 4) Architectural solutions based on an analog-to-digital computing, which can be used to solve the problems of the digital systems parallel analysis, have been further developed. 5) Vector-qubit structures of the logic data, that allow a quasi-parallel simulation of digital circuits, were proposed. Conclusions. Qubit models, quantum methods and combinatorial algorithms for technical diagnostics of digital devices have been implemented, which can significantly (up to 25%) reduce the time of test synthesis, deductive modeling of faulty and correct behavior, search for defective states by introducing an innovative idea of using qubit-vector data structures for describing logical components. Comparative assessments of qubit models and methods usage show an increase in the efficiency of algorithms for modeling digital devices compared to tabular ones. The superposition of a classical, quantum and analog computer is integrally represented, which allows to find the best solutions for recognition and decision making.

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Architectures of Quantum Memory-driven Computing

Cited by 2 publications

References 13 publications

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Quantum Digital-Analogue Computing

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