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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth
TL;DR
Shared classical randomness can enhance quantum generative models, allowing them to represent a broader range of distributions than purely unitary models at shallow depths. This discovery establishes a clear advantage for channel models over unitary counterparts in generating complex output distributions.
✦ Why It Matters
Consider integrating shared classical randomness into your quantum models to improve generative capabilities.
Key Takeaways
- Explore shared classical randomness to enhance quantum generative models.
- Utilize local Pauli operations for improved output distributions.
- Consider MBQC for practical implementation of randomness.
- Be aware of the depth limitations of purely unitary models.
How It Works
The study augments shallow unitary circuits by adding local Pauli operations, which are spatially separated and controlled by a single random bit. This integration allows the model to generate complex output distributions with long-range correlations, which are unattainable by purely unitary models at shallow depths.