Quantum Random Number Generation Using a Quanta Image Sensor

Amri, Emna; Felk, Yacine; Stucki, Damien; Ma, Jiaju; Fossum, Eric R.

doi:10.3390/s16071002

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…Some better-known examples are the electron-multiplying charge-coupled device (EMCCD) [22], [23], single-photon avalanche diode (SPAD) [11], [14], [24], Geiger-mode avalanche photodiode (GMAPD) [13], etc. The common feature of these devices is their single photon sensitivity, which makes them useful in medical imaging [25]- [27], astronomy [28], defense [29], nuclear engineering [30], depth and reflectivity reconstruction [31], ultra-fast lowlight tracking [32], and recently in quantum random number generation used in cryptography [33], [34].…”

Section: A Current State Of Qismentioning

confidence: 99%

Optimal Threshold Design for Quanta Image Sensor

Elgendy,

Chan

2017

Preprint

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Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the next generation image sensor after CCD and CMOS. Equipped with a massive number of single photon detectors, the sensor has a threshold q above which the number of arriving photons will trigger a binary response "1", or "0" otherwise. Existing methods in the device literature typically assume that q = 1 uniformly. We argue that a spatially varying threshold can significantly improve the signal-to-noise ratio of the reconstructed image. In this paper, we present an optimal threshold design framework. We make two contributions. First, we derive a set of oracle results to theoretically inform the maximally achievable performance. We show that the oracle threshold should match exactly with the underlying pixel intensity. Second, we show that around the oracle threshold there exists a set of thresholds that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior which allows us to develop a practical threshold update scheme using a bisection method. Experimentally, the new threshold design method achieves better rate of convergence than existing methods.

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Section: A Current State Of Qismentioning

confidence: 99%

Optimal Threshold Design for Quanta Image Sensor

Elgendy,

Chan

2017

Preprint

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“…The quantum random number generation (QRNG) presented here was realized from a photon-number measurement scheme. This scheme has been demonstrated through the use of conventional and quantum image sensors . Conventional image sensors with a high pixel density are widely integrated into consumer devices; however, they are incapable of detecting single photons.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the output data rate is significantly reduced. On the other hand, quantum image sensors with the capability of single photon detection offer an alternative solution for achieving a high-quality entropy source with the potential to generate high throughput . Unfortunately, the technology of quantum image sensors is still in a developing state, given that the implementation cost of the QRNG is expected to be expensive.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, quantum image sensors with the capability of single photon detection offer an alternative solution for achieving a highquality entropy source with the potential to generate high throughput. 33 Unfortunately, the technology of quantum image sensors is still in a developing state, 34 given that the implementation cost of the QRNG is expected to be expensive. Variations of QRNG realized from a photon-number measurement scheme have also been demonstrated using different photon counting technologies, including a photomultiplier tube (PMT), 28 an avalanched photodetector (APD), 31 and a silicon photomultiplier (SiPM).…”

Section: Introductionmentioning

confidence: 99%

“…This scheme has been demonstrated through the use of conventional 32 and quantum image sensors. 33 Conventional image sensors with a high pixel density are widely integrated into consumer devices; however, they are incapable of detecting single photons. The QRNG derived from conventional image sensors 32 encounters a technical obstacle caused by the low quality of an entropy source, necessitating postprocessing with a high compression ratio.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Random Number Generation Based on Multi-photon Detection

Aungskunsiri,

Jantarachote,

Wongpanya

et al. 2023

ACS Omega

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We demonstrate quantum random number generation based on a photon-number detection scheme with the use of a silicon photomultiplier. We implement a time integral with detector response signals for resolving photon numbers, which are subsequently digitized into a stream of 4-bit sequences with a generation rate of 13.6 Mbit/s. Our generated random bits pass the statistical randomness validation according to the U.S. National Institute of Standards and Technology (NIST) Special Publication 800-22. This scheme is implementable with inexpensive components, and the system can be miniaturized to the size of a plug-and-play portable cryptographic device.

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