Design of quantum optical experiments with logic artificial intelligence

TL;DR

This paper introduces a novel approach using Logic AI to design quantum optical experiments by encoding experimental setups as SAT problems, enabling more precise and interpretable solutions compared to traditional optimization methods.

Contribution

It presents a logic-based algorithm called Klaus for designing quantum experiments, demonstrating improved resolution over existing continuous optimization techniques.

Findings

Logic AI enhances the design process of quantum experiments.

Klaus provides interpretable photonic setup representations.

Logic-based methods outperform purely numerical approaches.

Abstract

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.