Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Bagherian, Maryam; Chehade, Sarah; Whitney, Ben; Passian, Ali

doi:10.1007/s00607-023-01154-0

Cited by 2 publications

(1 citation statement)

References 166 publications

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“…To make QC feasible on IoT endpoints, a multifaceted approach is required [25,26]. This entails advancing quantum technology to create smaller, more energyefficient quantum processors, developing quantum communication channels for IoT devices to tap into centralised QC resources, exploring hybrid computing models that combine classical and quantum processing, and optimising quantum algorithms for IoT-specific applications to reduce computational demands [27]. Additionally, research into energyefficient cooling systems, miniaturised quantum hardware, and secure QKD is essential.…”

Section: Faster Computation At Iot Endpoints/iot Nodes Etcmentioning

confidence: 99%

Quantum computing applications for Internet of Things

Peelam,

Rout,

Chamola

2023

IET Quantum Communication

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The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum‐secured smart lock etc.

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Section: Faster Computation At Iot Endpoints/iot Nodes Etcmentioning

confidence: 99%

Quantum computing applications for Internet of Things

Peelam,

Rout,

Chamola

2023

IET Quantum Communication

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Adaptive Quantization Range Division Technique for Electronic Control Data Compression in CNC Machine Tools

Hu,

Zhou,

Yang

et al. 2023

Electronics

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With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and storage due to their complex structure, high volume of data points, strong time-series characteristics, and large amounts of data. To address the shortcomings of existing compression algorithms in quantization methods for large amounts of data in the instruction-domain, this paper proposes a quantization method based on distortion rate evaluation and linear fitting entropy reduction transformation, which aims to compress state signals such as the load power and load current while ensuring the availability of the data. This approach provides technical support for the transmission of high-frequency big data and meets the lightweight data acquisition requirements of digital twins for CNC machine tools. Compared to the empirical approach, this approach was more accurate and more computationally efficient.

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Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Cited by 2 publications

References 166 publications

Quantum computing applications for Internet of Things

Quantum computing applications for Internet of Things

Adaptive Quantization Range Division Technique for Electronic Control Data Compression in CNC Machine Tools

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