Year: 2022
Type: article
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.
Authors: Ayesha Javed, Muhammad Arqam Raza, Sana Khalid
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219976038