# Quantum latent distributions in deep generative models

## TL;DR

This paper investigates how quantum-generated latent distributions can enhance deep generative models, providing theoretical insights and empirical evidence that quantum interference can improve data generation performance over classical methods.

## Contribution

The work offers a theoretical framework explaining when quantum latent distributions outperform classical ones and demonstrates their effectiveness through extensive benchmarking on real and synthetic datasets.

## Key findings

- Quantum interference distributions improve generative performance.
- Quantum latent distributions can produce data classical distributions cannot.
- Empirical results show advantages on real quantum hardware.

## Abstract

Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple latent distributions are often used, the choice of distribution has a strong impact on model performance. Recent experiments have suggested that the probability distributions produced by quantum processors, which are typically highly correlated and classically intractable, can lead to improved performance on some datasets. However, when and why latent distributions produced by quantum processors can improve performance, and whether these improvements are connected to quantum properties of these distributions, are open questions that we investigate in this work. We show in theory that, under certain conditions, these "quantum latent distributions" enable generative models to produce data distributions that classical latent distributions cannot efficiently produce. We provide intuition as to the underlying mechanisms that could explain a performance advantage on real datasets. Based on this, we perform extensive benchmarking on a synthetic quantum dataset and the QM9 molecular dataset, using both simulated and real photonic quantum processors. We find that the statistics arising from quantum interference lead to improved generative performance compared to classical baselines, suggesting that quantum processors can play a role in expanding the capabilities of deep generative models.