Quantum computer architectures: An idea whose time is not far away

Vignesh, R.; Poonacha, P.G.

doi:10.1109/ccoms.2015.7562843

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…e passed information is accessible to many qubits in a system, accelerating the speed of computation, unlike classical computation [31,32]. Trapped ion architecture, QC using superconducting qubits, and QC with nitrogen-vacancy center in diamonds are few of the hardware architectures considered for thorough research in well-equipped labs [33].…”

Section: Quantum Phenomenonmentioning

confidence: 99%

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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%

“…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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“…quantum computer. The notion of using NVs in the realization of quantum computers was introduced more than 20 years ago [5], and has been discussed in the literature on numerous occasions since then [6][7][8][9][10]. While obviously there are several possible schemes for the implementation of NV-based quantum computers, a feature common to many of them is the need to fabricate dense (∼10-20 nanometers apart) 1D vectors, or preferably 2D/3D arrays of NVs in well-defined (nanometer-scale) locations, as has been discussed previously for such and similar types of solid-state-based spin systems [11][12][13].…”

Section: Introduction 1nitrogen Vacancies In Diamondmentioning

confidence: 99%

Three-dimensional fourier imaging of thousands of individual solid-state quantum bits – a tool for spin-based quantum technology

Artzi¹,

Zgadzai²,

Solomon³

et al. 2023

Phys. Scr.

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Nitrogen vacancies in diamond (NVs) are frequently considered possible candidates to constitute the building blocks of spin-based quantum computers. The main caveats to this approach are the lack of a reliable process to accurately place many NVs in close proximity to each other (~10–20 nm) to enable an adequate spin-spin interaction; and the inability to read out and selectively manipulate the quantum states of many such closely spaced NVs. A possible approach to overcome these issues includes the following: (i) making use of a diamond dense with NVs in random ("as-produced") 3D positions; (ii) mapping out their individual locations at high spatial resolution (in 3D); (iii) employing techniques for selective spin manipulation based on the mapped 3D locations of the NVs; and (iv) making use of imaging techniques to read out the quantum state of the NVs. Within this grand vision, we present here a tool that can support this scheme—namely, an approach to the efficient high accuracy 3D mapping of many thousands of individual NVs in a diamond via magnetic resonance imaging (MRI). In the present work, the NVs’ spacings and the corresponding imaging resolution are in the submicron-scale, but the same approach can be scaled down to support a resolution lower than 10 nm in diamonds with dense NVs, as is required for practical quantum computing applications.

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