The work carried out in Q-ANNTENNA can be divided into four objectives:
The first objective is to deepen our understanding of the relationship between machine learning processes and tensor networks, which provide a faithful, efficient representation of some physically-relevant many-body low-energy quantum states. Thus, minimizing the energy of a many-body system is a key component in machine-learning methods, as well as in general optimization tasks. Here we have provided a new quantum algorithm to prepare ground states of quantum Hamiltonians using less qubits. Focusing on the most immediate short term, we have developed an heuristic version of it within the paradigm of the so-called NISQ (noisy, intermediate-scale quantum) devices. For this part, the tensor network library maintained at the host group has been crucial to the bench-marking of the algorithm. The results obtained have been exploited and disseminated in one publication, a code release, one invited talk in Canada, two contributed talks and two seminars.
The second objective of Q-ANNTENNA is to import physical insights from many-body quantum systems and quantum information, into machine learning processes. This has crystallized into several scientific results mostly in the line of efficient certification of quantum properties (Bell nonlocality, entanglement, correlations/depth quantification, self-testing), which have been disseminated through nine publications, two invited talks, one colloquium, two contributed talks and three seminars.
The third objective of Q-ANNTENNA is to study the renormalization procedure. In here we have developed several methods based on reinforcement learning to assist on the efficient certification of quantum properties. We have also put forward an efficient test to detect Bell correlations in many-body system. The scaling here is constant with the system size, therefore the method describes essentially the same object at different levels resolutions. The results obtained have been disseminated through two publications, two published conference abstracts, three invited talks, one contributed talk, six poster presentations, one seminar and one code release, besides numerous press releases.
The fourth objective of Q-ANNTENNA concerns experimental implementations; i.e. bridging the theory developed during the action with actual experimental platforms, assessing their capabilities and limitations. In an international collaboration, we probed an integrated photonics device technology. On the other hand, the shallow-depth quantum computing algorithm has allowed the fellow to initiate new collaborations with Harvard University, assessing the Rydberg atoms technology. The results obtained have been disseminated through one first-release publication in Science, two conference abstracts, and three seminars, including numerous press releases.
