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[2506.01666] Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Summary
This paper presents a multimodal denoising diffusion model for synthesizing discrete-continuous quantum circuits, improving efficiency in quantum operation compilation.
Why It Matters
As quantum computing advances, efficient compilation of quantum operations is crucial for scalability. This research introduces a novel approach that leverages machine learning to enhance circuit synthesis, potentially overcoming significant bottlenecks in current methodologies.
Key Takeaways
- The proposed model combines discrete gate selection and continuous parameter prediction for circuit synthesis.
- It outperforms existing methods in terms of gate counts and performance under noisy conditions.
- A post-optimization scheme significantly enhances the generated circuit ansätze.
- The model's rapid circuit generation facilitates the creation of large datasets for further research.
- Insights gained from the model can lead to new discoveries in quantum circuit synthesis.
Quantum Physics arXiv:2506.01666 (quant-ph) [Submitted on 2 Jun 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Synthesis of discrete-continuous quantum circuits with multimodal diffusion models Authors:Florian Fürrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Muñoz-Gil View a PDF of the paper titled Synthesis of discrete-continuous quantum circuits with multimodal diffusion models, by Florian F\"urrutter and 4 other authors View PDF HTML (experimental) 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 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 and circuit depths, showcasing the ability of the method to outperform existing approaches in ...