A tunable quantum random number generator based on a fiber-optical Sagnac interferometer

Abstract

Quantum random number generators (QRNG) are based on the naturally random measurement results performed on individual quantum systems. Here, we implement a branching-path photonic QRNG implemented with a Sagnac interferometer with a tunable splitting ratio. The fine-tuning of the splitting allows us to maximize the generated entropy of the sequence of produced random numbers and effectively compensate for tolerances in the components. By producing single-photons from attenuated telecom laser pulses, and employing commercially-available components we are able to generate a sequence of more than 2 gigabytes of random numbers with an average entropy of 7.99 bits/byte directly from the raw measured data. Furthermore, our sequence passes randomness tests from both the NIST and Dieharder statistical test suites, thus certifying its randomness. Our scheme shows an alternative design of QRNGs based on the dynamic adjustment of the uniformity of the produced random sequence, which is relevant for the construction of modern generators that rely on independent real-time testing of its performance.

Secure quantum random number generation with perovskite photonics

“…In this study 13 we show the feasibility of perovskite photonics as an alternative to silicon-based ditto by demonstrating a measurement-device independent quantum random number generator implementing the measurement-device independent protocol.…”

“…2, we achieve an average success probability for our test states of 98.35%. We generated over 8 Terabits of raw random data, which we subject to a Toeplitz hashing procedure 15,16 in order to remove any residual non-uniformities in the data. We seed the m × n Toeplitz matrix T with m + n − 1 bits from the start of the raw sequence, and subsequently multiply a vector of n raw bits with the matrix T to get m extracted bits.…”

“…The FPGA buffers the recorded bits and streams them to a host computer over an Ethernet connection for final storage, randomness extraction and assessment. 13 To realize the MDI protocol, we randomly switch between preparing and measuring states for randomness generation, and the eigenstates used for testing with a probability of 99% to generate random numbers and 0.5% to prepare and measure either of the eigenstates used for testing. Whenever we are testing the QRNG, we are not generating randomness, which thus incurs a penalty to the resulting generation rate, which is why we only test the system occasionally.…”