Design of quantum optical experiments with logic artificial intelligence
arXiv:2109.13273 · doi:10.22331/q-2022-10-13-836
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.
10 pages + appendices, 4 figures
References in corpus (10)
- Quantum algorithm for solving linear systems of equations
- Quantum computational advantage using photons
- A Quantum Approximate Optimization Algorithm
- Integrated Photonic Quantum Technologies
- Boson sampling with 20 input photons in 60-mode interferometers at state spaces
- The structure of multidimensional entanglement in multipartite systems
- Entanglement by Path Identity
- A scheme for universal high-dimensional quantum computation with linear optics
- Three Modern Roles for Logic in AI
- Questions on the Structure of Perfect Matchings inspired by Quantum Physics
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- Neural networks with quantum states of light
- Graph-theoretic insights on the constructability of complex entangled states