Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles state-by-state is prohibitive in both variational and fault-tolerant settings, thereby motivating a generative modeling approach. We introduce latent-conditioned parameterized quantum circuits (LPQCs), a hybrid quantum-classical framework in which classical neural networks map a latent variable sampled from a prior distribution to the parameters of a parameterized quantum circuit. We prove that LPQCs are universal approximators for probability measures over density...
| Authors | Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo et al. |
|---|---|
| Primary category | quant-ph |
| Published | 27 May 2026 |
| Last updated at source | 17 June 2026 |
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