Scalable quantum simulation of continuous-time generative models via tensor networks
Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein folding to emerging adoption for modeling language, time series, and quantum states. After training, inferring statistical properties from continuous-time models is costly. Wavefunction flows target this cost by recasting learned transport as unitary evolution, whose final Born distribution approximates the target distribution. This prepares a coherent amplitude encoding (a qsample) that can be post-processed by quantum algorithms offering a quadratic advantage over Monte Carlo sampling. We present the first numerical study of these flows, i...
| Authors | Nathan X. Kodama, L. Andrew Wray, Sam Cochran et al. |
|---|---|
| Primary category | quant-ph |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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