Authors:
Ghefar Alaa Aldeen Alrefai, Jouma Ali Al-Mohamad, Christo Ananth, Abdalla Nashat, Abdelrahman Hassan, Yusef Sherif

Addresses:
Department of Informatics and Communications Engineering, Faculty of Engineering, EBLA Private University, Idlib, Idlib Governorate, Syria. Department of Computer and Mobile Communication Engineering, Faculty of Information Engineering, Al-Shahbaa Private University, Aleppo, Aleppo Governorate, Syria. Department of Electronics and Communication Engineering, Samarkand State University, Samarkand, Samarkand Region, Uzbekistan. Department of Computer Science, Zewail City of Science and Technology, 6th of October City, Giza Governorate, Egypt.

Abstract:

Quantum machine learning sits at the intersection of two paradigm-shifting technologies: quantum computing and deep learning. By exploiting quantum mechanical phenomena – superposition, entanglement, and interference – QML promises exponential or quadratic speedups over classical algorithms for certain learning tasks, as well as the ability to model quantum data natively. However, the path to practical quantum advantage remains contested, constrained by noisy intermediate-scale quantum (NISQ) hardware, limited qubit counts (50–1,000), coherence times (50–500 μs), and high gate error rates (10⁻³–10⁻⁴). This paper provides a rigorous, technically deep survey of QML: mathematical foundations (Hilbert spaces, unitary evolution, measurement, quantum circuits), canonical algorithms (quantum kernel methods, variational quantum circuits (VQCs), quantum neural networks (QNNs), QSVMs, quantum generative adversarial networks (QGANs), encoding strategies (angle, amplitude, basis, and Hamiltonian encoding), optimization landscapes (barren plateaus, parameter shift rules, ansatz design), hardware aware implementation on superconducting (IBM, Google), trapped ion (IonQ), and neutral atom (Pasqal) platforms, empirical benchmarks on near term devices for classification, generative modeling, and quantum chemistry, and pathways to fault tolerant quantum machine learning (FTQML) with error correction (surface codes, logical qubits). Researchers critically assess claims of quantum advantage, identify scenarios where QML may outperform classical ML (small data, high-dimensional feature spaces from quantum systems, kernel estimation), and propose concrete evaluation metrics (quantum-classical cross-validation, resource estimation).

Keywords: Quantum Machine Learning (QML); Ansatz Design; Variational Quantum Circuits (VQCs); Encoding Strategies; Resource Estimation; Hamiltonian Encoding; Quantum Support Vector Machines (QSVMs).

Received on: 19/06/2025, Revised on: 06/09/2025, Accepted on: 21/10/2025, Published on: 15/08/2026

DOI: 10.69888/FTSIN.2026.000736

FMDB Transactions on Sustainable Intelligent Networks, 2026 Vol. 3 No. 3, Pages: 164-178

• Views : 43
• Downloads : 7

Download PDF