We demonstrated a QRNG using single photons from a quantum dot coupled to an optical nanofiber. The system generates certified random bits through path-encoded qubit states verified by NIST statistical tests.
Abstract Quantum random number generators (QRNGs) are based on naturally random measurement results performed on individual quantum systems. Here, we demonstrate a branching-path photonic QRNG implemented using a Sagnac interferometer with a tunable splitting ratio. The fine-tuning of the splitting ratio allows us to maximize the entropy of the generated sequence of 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.
Quantum random number generators (QRNG) are the prime source of randomness, by exploiting the unpredictable nature of Quantum mechanics QRNG aims to provide a string of completely random utterly unpredictable numbers. QRNGs are crucial for cryptography, scientific calculation, simulation, and many more real-world applications. Our Optical Quantum Random Number Generator (OQRNG) utilizes a high-speed pulsating laser, a Polarizing beam splitter (PBS), photodetectors, and advanced signal processing units that capture and digitize random photon intensity forming the raw random bit stream. This technique is famously known as Branching Path Technique. After that, we implement rigorous testing that encompasses the statistical randomness tests developed by National Institute of Standards and Technology (NIST) to ensure the guaranteed ran-domness meets security standards. A user-friendly Graphic user interface (GUI) simplifies interaction with the QRNG allowing the user to configure device settings, monitor system performance, and securely extract random numbers. Our findings help us demonstrate the superiority of QRNGs over classical random number generators, and highlight the potential of photon-based QRNGs and their environmentally friendly and efficient random number generation.
Abstract Measurement outcomes on quantum systems exhibit inherent randomness and are fundamentally nondeterministic. This has enabled quantum physics to set new standards for the generation of true randomness with significant applications in the fields of cryptography, statistical simulations, and modeling of the nondeterministic behavior in various other fields. In this work, we present a scheme for the generation of multi-bit random numbers using path-entangled single photons. For the experimental demonstration, we generate a path-entangled state using single photons from spontaneous parametric down-conversion (SPDC) and assign a multi-qubit state for them in path basis. One-bit and two-bit random numbers are then generated by measuring entangled states in the path basis. In addition to passing the NIST tests for randomness, we also demonstrate the certification of quantumness and self-certification of quantum random number generator (QRNG) using Clauser, Horne, Shimony and Holt (CHSH) inequality violation. We also record the significantly low autocorrelation coefficient from the raw bits generated and this along with CHSH violation rules out multi-photon events and ensure the protection from photon splitting attack. Distribution of photons along multiple paths resulting in multiple bits from one photon extends the limit on bit generation rate imposed by the detection dead time of the individual detector. Thus, the path-entangled states can generate higher bitrates compared to scheme using entangled photon pair which are limited by the coincidence counts. We demonstrate this by generating a high rate of about 80 Mbps when the single photon detector saturates at around 28 Mcps and still show violation of CHSH inequality.
Random numbers find applications in a range of different fields, from quantum key distribution and classical cryptography to fundamental science.They also find extensive use in gambling and lotteries.By exploiting the probabilistic nature of Quantum Mechanics, quantum random number generators (QRNGs) provide a secure and efficient means to produce random numbers.Most of the quantum random number generators demonstrated so far have been built in bulk optics, either using free space or fibre-optic components.While showing good performance, most of these demonstrations are strongly limited in real life applications, due to issues such as size, costs and the manufacturing process.In this thesis I report the demonstration of three different QRNGs based on integrated photonics.First, I demonstrated a QRNG based on homodyne measurement of optical vacuum states on a Silicon-on-insulator (SOI) chip.Second, I developed a SOI QRNG based on phase fluctuations from a laser diode.In these two schemes all the optical and opto-electronic components, excluding the laser, were integrated onto a silicon-on-insulator device.These schemes, being built on a silicon-on-insulator chip are potentially CMOS compatible and pave the way for being integrated onto other more complex systems.These QRNGs showed Gbps generation rates and passed the statistical tests provided by NIST.Third, I report the preliminary study of a QRNG based on homodyne measurement of optical vacuum states onto a Indium Phosphide (InP) chip.In this third experiment, all the components, including a laser diode, were monolithically integrated in the same chip, which provide a great advantage in terms of the overall size of the optics of the device.First, I would like to thank Jonathan, for the opportunity to pursue my PhD and for guiding my research in these four years.Thank you for giving me the chance to follow my curiosity and for pushing me to get the best out of my research.Thank you, Dylan, for being so helpful, always ready to answer my questions.I would like to thank Giacomo for sharing a big part of my research in these years
Random Number Generators (RNGs) are used in various applications, including cryptography, scientific simulations, gaming, and virtual reality. The quality of these random numbers is crucial, especially for computational approaches that rely on unpredictability to produce accurate results. In this thesis, we perform a comprehensive assessment of the quality of a novel Quantum Random Number Generator (QRNG) based on single photon. QRNGs leverage quantum mechanical properties to produce truly random numbers. This ensures high entropy, unpredictability, and security, making QRNGs particularly useful in fields that require high-quality randomness.We evaluate the statistical properties of strings generated by our QRNG and then its practical application by comparing it to traditional Pseudo-Random Number Generators (PRNGs) in Monte Carlo Integration (MCI), a technique that estimates integrals using random sampling. In particular, we use two statistical randomness test suits, namely NIST and Dieharder, to assess the statistical properties of our QRNG. All tests within these tests were passed, confirming that the corresponding QRNG achieves true randomness with sufficient high entropy. Next, we use our QRNG-generated numbers to seed Monte Carlo Integration (MCI) and compare it against results obtained using classical PRNG. The QRNG-based MCI demonstrated more accurate and stable numerical estimates, showing lower error rates and improved convergence compared to PRNG-based simulations.These findings highlight the advantages of QRNGs in enhancing numerical accuracy and computational security. While QRNGs may have slower generation rates than PRNGs, their superior randomness quality allows for more reliable Monte Carlo-based simulations, suggesting that fewer samples may be needed to achieve the same level of accuracy compared to classical PRNGs. This research underscores the potential of QRNG-powered numerical methods, paving the way for their integration into more advanced applications, including scientific computing, financial modeling, and cryptographic security.