Probabilistic modeling over permutations using quantum computers
Quantum computers provide a super-exponential speedup for performing a Fourier transform over the symmetric group, an ability for which practical use cases have remained elusive so far. In this work, we leverage this ability to unlock spectral methods for machine learning over permutation-structured data, which appear in applications such as multi-object tracking and recommendation systems. It has been shown previously that a powerful way of building probabilistic models over permutations is to use the framework of non-Abelian harmonic analysis, as the model's group Fourier spectrum captures the interaction complexity: "low frequencies" correspond to low order correlations, and "high freq...
| Authors | Vasilis Belis, Giulio Crognaletti, Matteo Argenton et al. |
|---|---|
| Primary category | quant-ph |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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