Simulation-Based Quantum System Inference with Neural Posterior Estimation
Models of quantum systems faithfully map system parameters to observations, but the inverse problem of parameter inference from measurement data presents a fundamental challenge: computationally intractable likelihoods due to an exponentially large Hilbert space. Here, we introduce simulation-based quantum system inference, a unified, likelihood-free framework that learns parameter posteriors directly from classical simulation data. The central idea is to pair polynomial-cost classical simulators, such as Pauli propagation and tensor networks, with normalizing flows or other neural density estimators for accurate, reusable inference. A single model, trained once, maps any new measurement ...
| Authors | Hang Zou, Anton Frisk Kockum, Martin Rahm et al. |
|---|---|
| Primary category | quant-ph |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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