Synthesis of discrete-continuous quantum circuits with multimodal diffusion models

arXiv:2506.01666v3 Announce Type: replace-cross Abstract: Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alterna