A Survey on Quantum Machine Learning - Basics, Current Trends, Challenges, Opportunities, and the Road Ahead

Aug 4, 2026

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Quantum computing (QC) is positioned as a path to improved efficiency for solving complex problems beyond classical computing. When QC is integrated with machine learning (ML), quantum machine learning (QML) emerges as a unified framework. This survey consolidates foundational QC concepts and highlights key advantages relative to classical approaches. It reviews representative QML algorithms, examines quantum datasets and their distinguishing properties, and summarizes advances in quantum hardware, alongside available software tools and simulators. It also surveys real-world applications and the potential to outperform classical ML methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

AccessFree to download

Introduction

Motivation from machine learning compute limits

Quantum computing as a new computing paradigm

Emergence of the quantum machine learning paradigm

Overview and comparison with related surveys

A Survey on Quantum Machine Learning: Basics, Current Trends, Challenges, Opportunities, and the Road Ahead Kamila Zaman 1,2 , Alberto Marchisio 1,2 , Muhammad Abdullah Hanif1,2 , and Muhammad Shafique 1,2 1eBrain Lab, Division of Engineering, New York University Abu Dhabi, UAE 2 Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, New York University Abu Dhabi, UAE ([e-mail: kz2137@nyu.edu](e-mail: kz2137@nyu.edu), [alberto.marchisio@nyu.edu](alberto.marchisio@nyu.edu), [mh6117@nyu.edu](mh6117@nyu.edu), [muhammad.shafique@nyu.edu](muhammad.shafique@nyu.edu)) arXiv :2310 . 10315v2 [ quant-ph] 27 Jul 2024 Abstract—Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This paper serves asa valuable resource for readers seeking to understand the current state-of-the-art techniques in the QML field, offering a solid foundation to embark on further exploration and development in this rapidly evolving area. Index Terms—Quantum Computing, Quantum Computer, Quantum Machine Learning, Quantum Neural Networks, Machine Learning, Neural Networks, Quantum Supremacy, Qubit, Superposition, Quantum Correlation, Entanglement, Quantum Gate, Quantum Circuit, Quantum Noise, Noisy Intermediate-Scale Quantum, Fault-Tolerant Quantum Computing, Parametrized Quantum Circuits, Quantum Annealing, Quantum Kernels, Variational Quantum Eigensolver, Quantum Data, Quantum Encoding, Quantum Datasets, Quantum Hardware, Quantum Simulator, Qiskit, PennyLane, Quantum Applications. I. INTRODUCTION Machine Learning (ML) systems are well-established tools for identifying patterns in data and generalizing complex, nonlinear problems. These systems have found applications in various domains, including computer vision, healthcare, finance, and the automotive industry. However, ML practitioners must navigate the substantial computing resources required to run large ML models. Despite employing numerous hardware-aware optimizations such as compression and approximations [1]–[5], the limitations of current computing infrastructures and technologies constrain the computational capabilities of ML systems. The high computational demands of modern ML models necessitate advanced hardware development. According to Moore’s law, the number of transistors on an integrated circuit doubles approximately every two years. However, this trend has reached its limits with current high-end CPUs and GPUs [6] . This physical saturation restricts computational power, leading to delays in processing, developmental, and scientific discovery within the ML community. For instance, training Large Language Models with hundreds of billions of parameters and trillions of tokens is extremely computeintensive [7] . Such tasks require massive investments in time and hardware resources, which only a few high-end companies can afford. To overcome these physical limits and support further discoveries, there is an urgent need to expl

FAQs

What does the survey cover about quantum machine learning (QML)?

It explains foundational quantum computing concepts, reviews QML algorithms and their applicability, and discusses quantum datasets, hardware progress, and available software tools and simulators. It also highlights practical applications and comparisons with related survey work.

Why is quantum computing considered beneficial compared with classical computing for complex problems?

Quantum computers leverage quantum mechanical phenomena to improve performance and information processing. The survey emphasizes potential advantages in optimization, time efficiency, and cost-effective solutions.

How does the survey address the computing limitations that affect modern machine learning?

It discusses the growing computational demand of large ML models and the physical limits of current CPU/GPU infrastructure, motivating the need for new hardware avenues to improve computational efficiency.

Aug 4, 2026

Upload by Oliver