Design of quantum optical experiments with logic artificial intelligence
This work addresses the challenge of designing precise quantum optical experiments for physicists, offering a formal and exact alternative to approximate machine learning approaches, though it appears incremental by combining logic and numeric strategies.
The authors tackled the problem of designing optical quantum experiments to prepare arbitrary quantum states by proposing a logic AI-based algorithm called Klaus, which maps the problem to a SAT formulation and finds interpretable photonic setups, showing significant improvement over state-of-the-art continuous optimization methods.
Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.