Syed Muhamad Assad

An integrated chip platform for CV-QKD system based on time and polarization multiplexing is designed and demonstrated. A proof-of-principle test is conducted, which shows the measurement results for key components. The secure key rate by simulation can reach 4 kbit/s at 40 km distance per transmission band.

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.

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.

Two coveted qualities for a random number generator (RNG) are uniformity and unpredictability. A Pseudo-RNG (PRNG) produces a uniform output, but it is predictable when one has knowledge of the seed and implementation parameters. While a quantum-RNG (QRNG) produces an unpredictable output, it is not necessarily uniform and hence typically requires randomness extraction. We examine these two aspects in RNGs by utilizing a machine learning cryptanalysis, showing the applicability of the tool in uncovering hidden correlations and implementation failures.

We demonstrate continuous variable Gaussian entanglement distillation using a measurement-based noiseless linear amplifier. Our scheme relies on a heterodyne detection scheme, followed by a classical non-deterministic post selection of the measurements. We demonstrate that we are able to extract a subset of data presenting a stronger entanglement than the original resource, and that we can recover entanglement after transmission loss.