Optimization of Neural Networks Using Quantum Computing Techniques

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ID: 309125
2022
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Ranked #35 of 35 articles by views in International journal of advanced sciences and computing

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Abstract
Quantum computing offers new algorithmic primitives—superposition, entanglement, and interference—that can accelerate or qualitatively change parts of the machine-learning pipeline. This article surveys optimization of neural networks using quantum techniques, focusing on three avenues: (i) quantum-accelerated linear algebra and sampling to speed up gradient and Hessian-vector computations; (ii) hybrid variational algorithms where parameterized quantum circuits (PQCs) act as feature maps or layers within classical networks; and (iii) quantum-aware optimizers such as the quantum natural gradient (QNG) that respect the geometry of quantum states. We synthesize theoretical results with NISQ-era constraints (noise, barren plateaus, trainability), propose design patterns for end-to-end systems, and present an illustrative comparison of convergence between classical SGD and a hybrid QNG setup. The analysis indicates that, for structured data and appropriately shallow, problem-matched ansätze, hybrid methods can improve sample efficiency and generalization while remaining hardware-feasible.
Reference Key
imported_1761903499_6904838b2aba8 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ayesha Javed, Muhammad Arqam Raza, Sana Khalid
Journal International journal of advanced sciences and computing
Year 2022
DOI
DOI not found
URL
https://ijasc.com/index.php/ijasc/article/view/7
Keywords Keywords not found

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