Quantum Entanglement and Teleportation Based on Silicon Photonics

Ding, Yunhong; Llewellyn, D.; Faruque, Imad I.; Bacco, Davide; Rottwitt, Karsten; Thompson, Mark G.; Wang, J.; Oxenløwe, Leif Katsuo

doi:10.1109/icton51198.2020.9203437

Cited by 6 publications

(12 citation statements)

References 18 publications

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“…For this reason, many materializations PNN support only the inference stage and the weights are taken with the use of software applications on the level of electronic operation. Moreover, some applications cannot be trained at all, as in [40], [42], [82], [83]. These architectures are very fast and efficient as far as energy consumption is concerned, but they are not flexible, as they are especially designed for specific applications as their weights consolidate in the material during their construction.…”

Section: Training Methodologiesmentioning

confidence: 99%

“…On the other hand, Indium Phosphide allows the materialization of monolithically integrated solutions, which include a combination of passive and active devices, such as laser and amplifiers. Moreover, the integrated photonic technology offers the prospect of reducing the order of magnitude of the integration into nano-levels with all the significant advantages that the forementioned reduction of size brings about, such as the reduction of energy footprint, smaller size etc [40], [42].…”

Section: Photonic Neuromorphic Processorsmentioning

confidence: 99%

“…The non-linear operation is accomplished through Electromagnetically Induced Transparency (EIT) which is based on quantum phenomena and is produced by lasercooled atoms 85 Rb, in a Magneto-Optical Trap (MOT). The materialization of this particular architecture is shown in Figure 3 [40], [42], [54], [55]Error! Reference source not found.…”

Section: Perceptronmentioning

confidence: 99%

“…The weights 𝑊 are controlled by external parameters until the network reaches the ideal output and be led to a condition of stable weights (training), where [19], [42], [49], [52]:…”

Section: Deep Pnnmentioning

confidence: 99%

“…In optical materializations, some basic restrictions to the control of weights are present, which have to be taken into account 𝑤 0. There cannot be a negative weight value since there is no negative light intensity value [42], [49], [64]:…”

Section: δ𝑤 =mentioning

confidence: 99%

See 4 more Smart Citations

A Comprehensive Survey on Nanophotonics Neural Networks

Demertzis¹,

Παπαδόπουλος²,

Iliadis³

et al. 2021

Preprint

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: In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.

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Section: Training Methodologiesmentioning

confidence: 99%

Section: Photonic Neuromorphic Processorsmentioning

confidence: 99%

Section: Perceptronmentioning

confidence: 99%

“…The weights 𝑊 are controlled by external parameters until the network reaches the ideal output and be led to a condition of stable weights (training), where [19], [42], [49], [52]:…”

Section: Deep Pnnmentioning

confidence: 99%