title: Synthesis of discrete-continuous quantum circuits with multimodal
    diffusion models
summary: 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 alternative,
  though they are currently restricted to discrete gate sets. Here, we introduce
  a multimodal denoising diffusion model that simultaneously generates a
  circuit's structure and its continuous parameters for compiling a target
  unitary. It leverages two independent diffusion processes, one for discrete
  gate selection and one for parameter prediction. We benchmark the model over
  different experiments, analyzing the method's accuracy across varying qubit
  counts, circuit depths, and proportions of parameterized gates. Finally, by
  exploiting its rapid circuit generation, we create large datasets of circuits
  for particular operations and use these to extract valuable heuristics that can
  help us discover new insights into quantum circuit synthesis.
paper_authors._id: 683eb172e34a45d302c2439d
paper_authors.name: Florian Fürrutter
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T09:16:37.160Z
paper_authors._id: 638a67f6fc10b2be222531cd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1670014915088-noauth.png
paper_authors.fullname: Florian Fürrutter
paper_authors.type: user
paper_authors.user: Floki00
paper_authors._id: 683eb172e34a45d302c2439e
paper_authors.name: Zohim Chandani
paper_authors._id: 683eb172e34a45d302c2439f
paper_authors.name: Ikko Hamamura
paper_authors._id: 683eb172e34a45d302c243a0
paper_authors.name: Hans J. Briegel
paper_authors._id: 683eb172e34a45d302c243a1
paper_authors.name: Gorka Muñoz-Gil
paper_title: Synthesis of discrete-continuous quantum circuits with multimodal
    diffusion models
paper_summary: 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 alternative,
  though they are currently restricted to discrete gate sets. Here, we introduce
  a multimodal denoising diffusion model that simultaneously generates a
  circuit's structure and its continuous parameters for compiling a target
  unitary. It leverages two independent diffusion processes, one for discrete
  gate selection and one for parameter prediction. We benchmark the model over
  different experiments, analyzing the method's accuracy across varying qubit
  counts, circuit depths, and proportions of parameterized gates. Finally, by
  exploiting its rapid circuit generation, we create large datasets of circuits
  for particular operations and use these to extract valuable heuristics that can
  help us discover new insights into quantum circuit synthesis.
paper_ai_summary: A multimodal denoising diffusion model is introduced for generating both the structure and continuous parameters of quantum circuits, offering an efficient alternative to traditional quantum operation compilation methods.
paper_ai_keywords: denoising diffusion model circuit generation quantum operations quantum circuit synthesis discrete gate selection parameter prediction quantum hardware classical simulations quantum circuit structure continuous parameters qubit counts circuit depths parameterized gates