Martin Plesch

High-dimensional entanglement promises to increase the information capacity of photons and isnow routinely generated exploiting spatio-temporal degrees of freedom of single photons. A curiousfeature of these systems is the possibility to certify entanglement despite strong noise in the data.We show that it is also possible to exploit this noisy entanglement by introducing a protocol thatuses mutliple subspaces of the high-dimensional system simultaneously. Our protocol can be used toincrease key rates in realistic conditions. To that end, we conduct two simulations of our protocol fornoise models that apply to the two most commonly used sources of high-dimensional entanglement:time-bins and spatial modes.

Our ability to trust that a random number is truly random is essential for fields as diverse as cryptography and fundamental tests of quantum mechanics. Device-independent quantum random number generators (QRNGs) provide a means of completely trusted randomness, but are highly impractical due to their strict technological requirements, such as loophole-free quantum nonlocality. By making fixed assumptions on specific parts of the device, semi-device-independent QRNGs lower these requirements drastically. However, this {has usually been} done at the cost of limiting their flexibility and security to a specific physical implementation and level of trust. Here we propose and experimentally test a new framework for semi-device-independent randomness certification that employs a flexible set of assumptions, allowing it to be applied in a range of physical scenarios involving both quantum and classical entropy sources. At the heart of our method lies a source of trusted vacuum in the form of a signal shutter, which enables the honesty of partially trusted measurement devices to be tested and provides lower bounds on the guessing probability of their measurement outcomes. We experimentally verify our protocol with a photonic setup and generate secure random bits under three different source assumptions with varying degrees of security and resulting data rates. Our work demonstrates a simple and practical way for achieving semi-device-independent randomness generation with user-defined flexibility in terms of levels of trust and physical implementations.