OpenQKD: Testbed for quantum key distribution
OpenQKD is a European Horizon 2020 project to test quantum key distribution (QKD) technology and to prepare a pan-European QKD deployment to protect European citizens and economies against the potential security threat posed by a quantum computer. The three-year project started in September 2019. With a budget of €18 million, 38 European partners are developing fibre-based and free-space QKD and deploying over 30 use cases at 16 sites across Europe. Among these, several use cases are planned in Geneva. One of them is the so-called Quantum Vault, which aims to protect digital assets against failures and attacks. As a proof of concept, the Quantum Vault is being realised in six Genevan datacentres and telecom nodes. CERN openlab and the Poznan Supercomputing and Networking Center in Poland are supporing the Quantum Vault by hosting one node at the CERN Data Centre. It is planned to actively involve CERN openlab by running a proper use case taking advantage of the Quantum Vault infrastructure.
Quantum generative adversarial networks
Generative adversarial networks (GANs) are among the most interesting models in classical machine learning. GANs are an example of generative models, i.e. models that learn a hidden distribution from the training dataset, and can sample new synthetic data. At CERN openlab, we have been investigating their use as an alternative to Monte Carlo simulation, obtaining remarkable results. Much faster than standard Monte Carlo algorithms, GANs can generate realistic synthetic data, while retaining a high level of accuracy (see our fast simulation project). Quantum GANs could have more representational power than classical GANs, making them better able to learn more complex distributions from smaller training datasets. We are now training a quantum GAN to generate images of a few pixels and we are investigating two possible approaches: a hybrid schema with a quantum generator learning the target PDF, using either a classical network or a vibrational quantum circuit as a discriminator (variational quantum generator), as well as a full quantum adversarial implementation (quGAN).
Track seeding optimisation
The Kalman filter is widely used in high-energy physics for track fitting of particle trajectories. It runs after an initial pattern-recognition step where detector ‘hits’ are clustered into subsets belonging to the same particle. Currently, several pattern recognition approaches exist. Quantum computing can be used to reduce the initial combinatorial search using the ‘quantum approximate optimisation algorithm’ developed by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge, US. We are now studying the application of what is known as the ‘variational-quantum-eigensolver algorithm’ and implementing it on Intel’s quantum simulator.
Quantum homomorphic encryption
The latest advances in machine learning and data analytics offer great potential for gaining new insights from medical data. However, data privacy is of paramount concern. Anonymisation via the removal of personal information is not an option, since medical records carry information that may allow the identification of the owner much more easily and securely than the name or the birth date. One possibility being studied is to encrypt sensitive information in such a way that makes data analytics possible without decryption. This is called homomorphic encryption. It is important to find an encryption strategy that is secure, while also ensuring it is possible to apply a large family of analytic algorithms to the data. While such encryption algorithms do exist, they require high-quality random numbers and they tend to be very demanding in terms of computing resources. Thus, this is a promising field of investigation for the utilisation of quantum computing.
The aims of the project are multiple: to transfer anonymized medical records protected by Quantum Keys, to develop a quantum homomorphic encryption (QHE) algorithm to apply on it and to analyse the data with QHE-friendly analysis tools (techniques based on machine-learning or deep-learning). The main project consists of four different parts, each realised in collaboration with different partners, both European and Korean: ID Quantique, Innosuisse (the Swiss Innovation Agency), Korea Institute of Science and Technology Information (KISTI), and the Seoul National University Bundang Hospital (SNUBH).
Quantum random number generator
We have recently established a collaboration with Cambridge Quantum Computing to test the performance of a new quantum random number generators and study its integration within simulation software used in high-energy physics.
RandomPower: evaluating the impact of a low-cost, robust ‘true random power generator’
Researchers at the University of Insubria in Italy have invented a true random power generator (TRNG). This is based on the local analysis of the time series of endogenous self-amplified pulses in a specific silicon device. The principle has been validated with lab equipment and a low cost, small-form-factor board has been developed and commissioned with the support of an ATTRACT project. The board can deliver a stream of unpredictable bits at frequencies currently up to 1Mbps for a single generator, with the possibility to be scaled up. Randomness has been qualified through a test suite from the US National Institute of Standards and Technology, as well as beyond this. Together with the CERN openlab, the University of Insubria intends to evaluate the impact of the TRNG availability in a set of use-cases, as follows:
- Modifying the Linux OS, replacing the embedded random number generation with the random power stream, in order to facilitate its adoption.
- Comparing the outcome of the application of generative adversarial networks using training sets guided by pseudo random number generators (PRNG) or the random power TRNG.
- Identifying classes of Monte Carlo simulations in high-energy physics where the use of PRNG could be particularly critical.
Moreover, the availability of a low-cost platform for high-quality random numbers may open up new possibilities in the use of homomorphic encryption, relevant for privacy-preserving data analysis; this will be thoroughly evaluated.