Bottom-Up Design of Quantum Optical Experiments Using Discrete Generative Models

Abstract:Designing quantum optical experiments requires searching over discrete circuit topologies and continuous parameters, often with multiple realizations of the same target state. Graph-based methods commonly address this problem by optimizing a dense graph and pruning it toward a single circuit. We introduce \texttt{Grinch}, a bottom-up reward-driven generative framework that learns to sample optical graphs directly from fidelity-based rewards without relying on a pre-existing training dataset. We demonstrate the framework on multipartite entangled states, graph and cluster states, and nonlocal photonic-gate targets, obtaining multiple high-fidelity optical graphs. We identify asymptotic solutions for states that cannot be generated exactly by graphs, as well as nonlocal Toffoli gate constructions requiring two ancillas. Our work presents hardware-constraint objectives, which lead to alternative constructions of CNOT and Toffoli gates when connections between ancilla nodes are restricted. These findings illustrate a reward-driven approach to quantum optical inverse design, exploring alternative circuit topologies while directly incorporating connectivity constraints into the search process.

arXiv:2609.08073 [quant-ph]

(or arXiv:2609.08073v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.08073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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