Neural quantum embedding via deterministic quantum computation with one qubit

TL;DR

This paper introduces a neural quantum embedding method using deterministic quantum computation with one qubit (DQC1), significantly improving data distinguishability and classification accuracy in quantum machine learning applications.

Contribution

The work presents a novel neural quantum embedding technique trained via DQC1, enabling efficient data embedding and high-accuracy classification on ensemble quantum systems like NMR.

Findings

Achieved 98% classification accuracy with NQE-DQC1

Demonstrated improved distinguishability over traditional methods

Validated encoding of handwritten images into NMR quantum processors

Abstract

Quantum computing is expected to provide exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as quantum data embedding, to maximize classification performance remains a critical challenge. In this work, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1). Unlike the traditional embedding approach, NQE trains a neural network to maximize the trace distance between quantum states corresponding to different categories of classical data. Furthermore, training is efficiently achieved using DQC1, which is specifically designed for ensemble quantum systems, such as nuclear magnetic resonance (NMR). We validate the NQE-DQC1 protocol by encoding handwritten images into NMR quantum processors, demonstrating a significant improvement in distinguishability compared to traditional methods. Additionally, after training the NQE, we implement a parameterized quantum circuit for classification tasks, achieving 98\% classification accuracy, in contrast to the 54\% accuracy obtained using traditional embedding. Moreover, we show that the NQE-DQC1 protocol is extendable, enabling the use of the NMR system for NQE training due to its high compatibility with DQC1, while subsequent machine learning tasks can be performed on other physical platforms, such as superconducting circuits. Our work opens new avenues for utilizing ensemble quantum systems for efficient classical data embedding into quantum registers.