Synthesis of discrete-continuous quantum circuits with multimodal
  diffusion models
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.
683eb172e34a45d302c2439d
Florian Fürrutter
claimed_verified
2025-06-03T09:16:37.160Z
638a67f6fc10b2be222531cd
https://cdn-avatars.huggingface.co/v1/production/uploads/1670014915088-noauth.png
Florian Fürrutter
user
Floki00
683eb172e34a45d302c2439e
Zohim Chandani
683eb172e34a45d302c2439f
Ikko Hamamura
683eb172e34a45d302c243a0
Hans J. Briegel
683eb172e34a45d302c243a1
Gorka Muñoz-Gil
Synthesis of discrete-continuous quantum circuits with multimodal
  diffusion models
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.
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.
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