ICFO encompasses numerous research groups, both experimental and theoretical, with expertise in quantum computing and simulation. Specifically, the quantum information, ultracold atoms, and quantum optics groups have been dedicated for years to the development of quantum simulators and algorithms capable of studying the most fundamental properties of matter. They also have experience in utilizing machine learning techniques for these purposes. Some examples of their recent works include Nature 608, 293-297 (2022), on experimental quantum simulation, Nature 600 (7890), 625-629 (2018), addressing fundamental questions in quantum mechanics, or the soon-to-be-published book “Modern applications of machine learning in quantum sciences.”
Development of new software for the control of quantum simulators of neutral atoms and the study of their applications for quantum machine learning algorithms: recognition of many-body quantum phases, characterization of their dynamics, classification of complex models based on unique trajectories (classical or quantum), interpretation of experimental data, and the development of theories on hybrid quantum neural networks, among others.
The activity consists of the following tasks:
1. New control software to facilitate the integration of new instrumentation into quantum simulators, enable greater process automation, and support remote control. This aims to make the implementation of machine learning algorithms feasible for optimizing their operation and control for the preparation of quantum states.
2. Design and analysis of experiments with validation methods accessible to current simulators, verification and certification of quantum simulators, and quantum optimizers/”annealers,” and the development of new theoretical physics methods necessary to achieve these objectives.