Contributed Talks 4a: Random-number generation (Chairs: Felix Bussieres and Hugo Zbinden)

Abstract: Random numbers are a fundamental ingredient in fields such as simulation, modeling, and cryptography. Good random numbers should be independent and uniformly distributed. Moreover, for cryptographic applications, they should also be unpredictable. A fundamental feature of quantum theory is that certain measurement outcomes are intrinsically random and unpredictable. These can be harnessed to provide unconditionally secure random numbers. We demonstrate a real-time self-testing source-independent quantum random-number generator (SI QRNG) that uses squeezed light as a source. We generate secure random numbers by measuring the quadratures of the electromagnetic field without making any assumptions about the source other than an energy bound; only the detection device is trusted. We use homodyne detection to measure alternately the Q and P conjugate quadratures of our source. P measurements allow us to estimate a bound on any classical or quantum side information that a malicious eavesdropper may obtain. This bound gives the minimum number of secure bits we can extract from the Q measurement. We discuss the performance of different estimators for this bound. We operate this QRNG with a squeezed-state source and compare its performance with a thermal-state source. This is a demonstration of a QRNG using a squeezed state, as well as an implementation of real-time quadrature switching for a SI QRNG.

Abstract: Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.

Abstract: We present an on-chip receiver for time-based quantum key distribution (QKD) protocols such as the three-state time-bin protocol. The device features fully integrated superconducting nanowire single-photon detectors (SNSPD), low-loss delay lines and broadband 3D fiber-to-chip couplers with a total footprint of 800x800µm^2 on a single chip. By using waveguide-integrated SNSPDs featuring small dead times and low dark-count rates we are able to operate at 2.5 GHz clock rates and achieve high performance without saturating the detector at short distances. The device is demonstrated to work for wavelengths from 1480 nm to 1610 nm, but can be easily adapted to also work at visible light (on the same chip).