GBS-Assisted Quantum Unsupervised Machine Learning on a Universal Programmable Integrated Quantum Chip

TL;DR

This paper demonstrates the first experimental use of a quantum algorithm called GBS for unsupervised machine learning on a programmable quantum chip, showing improved performance in feature extraction and data generation.

Contribution

First experimental implementation of GBS-assisted quantum unsupervised machine learning on a universal programmable photonic chip.

Findings

Quantum-enhanced feature extraction from high-dimensional data using GBS.

Improved performance in generating arbitrary curve points and reconstructing handwritten digits.

Demonstration of scalable quantum unsupervised learning with reduced training parameters.

Abstract

Quantum machine learning stands poised as a forefront application for near-term quantum devices, addressing scalability challenges posed by classical computers in handling large datasets. Gaussian boson sampling (GBS), an intricate quantum algorithm deemed computationally infeasible for classical counterparts, represents a substantial leap forward in computational tasks. However, to date, the benefits of GBS-assisted quantum unsupervised machine learning are not experimentally demonstrated. Here, we present the first experimental implementation of quantum unsupervised machine learning using the GBS protocol with a universal programmable integrated photonic chip. The experimental system contains 16 squeezing sources, a universal programmable unitary matrix network of 16 modes, and a multi-channel single-photon detector, producing substantial output data crucial for 2 typical types of unsupervised tasks: feature extraction and generative network. Compared to classical approaches, the study demonstrates quantum-enhanced capability in extracting complex features from high-dimensional spaces and improved performance in generating arbitrary curve points and reconstructing handwritten digit images. This work not only underscores the potential of GBS in expressing high-dimensional features but also charts a path toward practical implementations within scalable, dimension-enhanced quantum unsupervised machine learning frameworks. The quantum unsupervised machine learning paradigm, offering theoretical acceleration and reduced training parameters for high-dimensional datasets, shows significant promise for advancing real-world applications of quantum technologies.

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