| name | qst-flow-quantum-tomography |
| description | QST-Flow framework for continuous-variable quantum state tomography using flow-based generative modeling. Models Husimi-Q functions with QST-QFlow and Wigner functions with QST-WFlow as difference of normalized flows. Use when working with non-Gaussian bosonic states, phase-space tomography, or quantum state reconstruction. |
| metadata | {"arxiv_id":"2607.21584","published":"2026-07-23","authors":"Owen Dugan, Rumen Dangovski, Peter Y. Lu, Di Luo","tags":["quantum","tomography","flow-based","continuous-variable","Wigner","Husimi-Q"]} |
| license | Complete terms in LICENSE.txt |
QST-Flow: Flow-based Phase-space Tomography
Overview
QST-Flow is a quantum state tomography framework that uses flow-based generative modeling to represent experimentally accessible phase-space quasiprobability distributions. Instead of truncating density matrices or using fixed grids, QST-Flow represents quantum states with normalized, samplable neural densities.
Key Components
QST-QFlow
- Models the positive Husimi-Q function with a single normalizing flow
- Provides normalized probability model for coherent-state measurements
- Enables exact density evaluation and direct sampling
QST-WFlow
- Models sign-changing Wigner functions as a trainable difference of two normalized flows
- Explicitly captures negativity while preserving normalization
- Handles interference fringes and negative lobes that carry non-classical information
Methodology
- Phase-space representation: Work directly in continuous phase space without grid discretization
- Flow-based modeling: Use invertible transformations with tractable Jacobians
- Normalization preservation: Ensure quasiprobability distributions remain properly normalized
- Importance sampling: Learn from finite phase-space measurements without fixed grid constraints
- Difference-of-flows construction: For Wigner functions, represent as Q₁(α) - Q₂(α) where both Q₁ and Q₂ are normalized flows
Applications
- Single-mode non-Gaussian state reconstruction (cat, binomial, GKP, number, Fock states)
- Extension to multimode states
- Robust reconstruction from noisy Wigner data
- Measurement-efficient tomography of nonclassical bosonic systems
Advantages over Prior Methods
- Avoids Fock-basis truncation limitations
- No fixed grid resolution constraints
- Exact density evaluation and direct sampling capabilities
- Improved reconstruction error compared to QST-CGAN and other ML tomography methods
- Scalable to higher-dimensional phase spaces
Implementation Considerations
- Requires normalizing flow architectures with invertible transformations
- Training uses importance-sampled likelihood estimates from experimental measurements
- For Wigner functions, ensure both component flows are properly normalized
- Validate reconstruction quality using quantum fidelity bounds
Activation Keywords
- qst-flow
- quantum tomography flow
- phase-space tomography
- continuous-variable tomography
- Wigner flow modeling
- Husimi-Q flow
References
- Original paper: arXiv:2607.21584
- Related: quantum-state-tomography, flow-based-generative-modeling, continuous-variable-quantum
