Quantum computing is expected to provide an 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 Letter, 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 parametrized 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 Letter opens new avenues for utilizing ensemble quantum systems for efficient classical data embedding into quantum registers.

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• Machine learning
• Quantum algorithms & computation
• Quantum simulation