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  1. Home
  3. Towards Provably Efficient Quantum Algorithms For Large-scale Machine-learning Models.
  1. Home
  3. Towards Provably Efficient Quantum Algorithms For Large-scale Machine-learning Models.

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Towards provably efficient quantum algorithms for large-scale machine-learning models.

Junyu Liu1,2,3,4,5,6, Minzhao Liu7,8, Jin-Peng Liu9,10,11

  • 1Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA.

Nature Communications
|January 10, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Fault-tolerant quantum computing may offer efficient solutions for training large machine learning models. This approach shows potential for reducing computational costs in artificial intelligence, particularly for sparse and dissipative models.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Large machine learning models face significant computational challenges in training.
  • Current methods require substantial resources in terms of computation, power, and time.
  • Bottlenecks exist in both pre-training and fine-tuning phases of model development.

Purpose of the Study:

  • To investigate the potential of fault-tolerant quantum computing for optimizing gradient descent algorithms.
  • To demonstrate provably efficient resolutions for machine learning training using quantum methods.
  • To explore quantum enhancements for large-scale artificial intelligence models.

Main Methods:

  • Leveraging efficient quantum algorithms for dissipative differential equations.
  • Adapting these algorithms for generic (stochastic) gradient descent.
  • Benchmarking quantum approaches on large machine learning models (7M-103M parameters).
  • Analyzing performance under conditions of model sparsity and dissipation.

    • Main Results:

    • Fault-tolerant quantum computing can provide efficient solutions for gradient descent.
    • Quantum algorithms show a potential scaling advantage for training large models.
    • Quantum enhancement is observed in the early stages of sparse training post-pruning.
    • A scheme for sparse parameter download/re-upload is motivated.

    Conclusions:

    • Fault-tolerant quantum algorithms show promise for addressing computational bottlenecks in large-scale machine learning.
    • Quantum computing could significantly contribute to the efficiency of state-of-the-art AI.
    • The findings suggest a viable path towards more resource-efficient AI model training.