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Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective

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arxiv 2405.11304 v2 pith:FGP6T4HE submitted 2024-05-18 quant-ph

Chen-Yu Liu , En-Jui Kuo , Chu-Hsuan Abraham Lin , Jason Gemsun Young , Yeong-Jar Chang , Min-Hsiu Hsieh , Hsi-Sheng Goan This is my paper · ORCID
classification quant-ph
keywords learningmachinemodelquantumapproachclassicalcompressionpotential
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abstract

We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

    quant-ph 2025-09 reject novelty 5.0 of 10

    QKANs show strong empirical performance on regression, vision, and language tasks, but the claimed exponential parameter reduction is not rigorously established.

  2. Quantum Relational Knowledge Distillation

    quant-ph 2025-08 unverdicted novelty 5.0 of 10

    Quantum Relational Knowledge Distillation (QRKD) uses quantum kernel values between classical features as relational guidance, and the paper reports consistent student accuracy gains over classical RKD across MNIST, C...

  3. Enhancing Interpretability of Quantum-Assisted Blockchain Clustering via AI Agent-Based Qualitative Analysis

    quant-ph 2025-06 reject novelty 4.0 of 10

    A two-stage framework uses clustering metrics and an LLM-based agent to interpret quantum-assisted blockchain clustering, reporting K=3 as optimal on MCO2 transaction data.

  4. Hybrid Parameterized Quantum States for Variational Quantum Learning

    quant-ph 2025-05 conditional novelty 4.0 of 10

    HPQS is a weighted blend of PQC measurement estimates and neural quantum state predictions that improves finite-shot accuracy and perplexity on MNIST classification, Quantum-Train parameter generation, and LLM LoRA fi...

  5. Quantum computing and artificial intelligence: status and perspectives

    quant-ph 2025-05 unverdicted novelty 3.0 of 10

    A broad expert white paper sets a European research agenda for combining quantum computing and AI, spanning quantum machine learning, AI-driven quantum control, and foundational questions.

  6. Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data

    cs.LG 2025-05 reject novelty 3.0 of 10

    Quantum feature maps are reported to improve blockchain transaction clustering, but the comparison omits classical random features and the results are selected on the test set.

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