Training-efficient density quantum machine learning

TL;DR

This paper introduces density quantum neural networks that balance expressivity and trainability, enabling efficient quantum machine learning models with improved performance and hardware compatibility.

Contribution

The paper proposes a novel density quantum neural network framework that enhances expressivity and trainability, connecting various QML approaches and extending classical mixture models.

Findings

Density models achieve similar performance with shallower circuits.

Commuting-generator circuits enable efficient gradient extraction.

Numerical experiments validate improved model performance.

Abstract

Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model family that prepares mixtures of trainable unitaries, with a distributional constraint over coefficients. This framework balances expressivity and efficient trainability, especially on quantum hardware. For expressivity, the Hastings-Campbell Mixing lemma converts benefits from linear combination of unitaries into density models with similar performance guarantees but shallower circuits. For trainability, commuting-generator circuits enable density model construction with efficiently extractable gradients. The framework connects to various facets of QML including post-variational and measurement-based learning. In classical settings, density models naturally integrate the mixture of experts formalism, and offer natural overfitting mitigation. The framework is versatile - we uplift several quantum models into density versions to improve model performance, or trainability, or both. These include Hamming weight-preserving and equivariant models, among others. Extensive numerical experiments validate our findings.