Fock State-enhanced Expressivity of Quantum Machine Learning Models

TL;DR

This paper introduces a photonic-based quantum data-encoding scheme that enhances the expressivity of quantum machine learning models by utilizing Fock space and photon-number control, enabling efficient classification with fewer resources.

Contribution

It proposes a novel Fock state-based data encoding method that improves quantum machine learning expressivity without nonlinear components, and introduces three scalable classification techniques.

Findings

Fock state encoding increases model expressivity.

Photon-number control enables resource-efficient classification.

The methods are compatible with noisy intermediate-scale quantum devices.

Abstract

The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work shed some light on the unique advantages offers by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks.