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IEEE Nanotechnology Magazine

Hardware-Informed Applications, Application-Informed Hardware

Antonio A. Gentile, Shaheen Acheche, Atiyo Ghosh, Oleksandr Kyriienko, Louis-Paul Henry

Published Aug 26, 2025• 1 citation
Publisher

Abstract

MACHINE learning and Scientific Computing stand to gain significantly from Quantum Computing (QC), which holds promise to deliver new tools for solving complex and computationally demanding problems. Hardware-informed approaches, which attempt to lower the bar in resources required for the utilization of quantum paradigms, could accelerate such development. Simultaneously, as traditional workflows routinely employed in a number of applications evolve, so too do the opportunities for integrating quantum algorithms in the pipeline and unlock classical limitations. By developing the understanding for distinct advantages of quantum devices, new methods can emerge to enhance predictive accuracy, accelerate simulations, and uncover novel insights in scientific and industrial applications. However, limited resources offered by first generations of quantum devices typically necessitate of classical co-processing power, and require tailored software-hardware approach, often referred as ’co-design’. Furthermore, the nature of quantum computing and machine learning in science & technology applications increasingly call for methods that are physics-informed. This review explores the intersection of quantum computing and machine learning, with a particular emphasis on scientific machine learning (SciML). Unlike conventional machine learning, SciML is deeply rooted in formal and domain-specific constraints, making it an ideal candidate for quantum-enhanced approaches. Crucially, we argument how effective quantum algorithms need to both be aligned with the structures and requirements of targeted scientific problems, whilst carefully understanding the interplay between hardware and problem formulation. A key focus of this review is the role of neutral atomic quantum processors in advancing SciML. These platforms offer unique computational capabilities that remain under-explored compared to other quantum platforms. With their ability to naturally encode spatial and graph-based problems, neutral atom systems provide a promising avenue for tackling optimization and machine learning tasks that are particularly relevant in scientific computing. By highlighting in few examples the advantages and open questions surrounding these processors, we aim to present a forward-looking perspective on how quantum computing can reshape computational approaches in science and engineering.

DOI
10.1109/mnano.2025.3585999

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Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources. We also illustrate how machine learning can be used both to mitigate the effects of errors on presently available noisy intermediate-scale quantum devices, and to understand quantum advantage via an automatic study of quantum walk processes on graphs. In addition, we review how quantum hardware can be enhanced by applying machine learning to fundamental and applied physics problems as well as quantum tomography and photonics. We aim to demonstrate how concepts in physics can be translated into practical engineering of machine learning solutions using quantum software.
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 survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
The rapid advancement of artificial intelligence has positioned Machine Learning (ML) as a cornerstone technology across scientific and industrial domains. In parallel, the emergence of quantum computing has catalyzed a new interdisciplinary field—Quantum Machine Learning (QML)—which promises to transcend the computational barriers faced by classical algorithms. This review paper systematically examines the evolution of ML from foundational statistical models to contemporary transformer architectures, and provides a structured analysis of QML paradigms including variational quantum circuits (VQCs), quantum kernel methods, and quantum neural networks (QNNs). Drawing from 95+ peer-reviewed publications indexed in Scopus and Science Citation Index (SCI) journals—with particular emphasis on 2025–2026 publications in IEEE Access, Nature Communications, Nature Computational Science, and Physical Review Letters—we analyze performance benchmarks, identify hardware constraints, and outline algorithmic innovations shaping the near-term quantum advantage landscape. Our findings indicate that while classical ML retains superiority in large-scale perception tasks, QML demonstrates significant advantages in combinatorial optimization and quantum chemistry simulation. Recent 2025 advances in error mitigation on superconducting qubits and improved VQC barren-plateau mitigation are narrowing this gap rapidly. The paper concludes with a forward-looking research agenda covering fault-tolerant QML, hybrid architectures, and quantum NLP.
Quantum machine learning (QML) holds the potential to transform various industries, yet its widespread adoption faces formidable challenges. This paper provides a condensed exploration of these challenges and opportunities. I delve into: • Hardware Limitations: Present quantum computers are constrained in qubit count and gate quality, posing obstacles to real-world QML implementation. We dissect the implications of these limitations on QML computations. • Error Correction: Quantum systems are prone to errors stemming from hardware noise and gate imperfections. We scrutinize the strategies to mitigate these errors and enhance QML accuracy. • Algorithm Development: The evolution of QML algorithms is examined, including computational complexity issues and adaptability to noisy quantum hardware. then pivot to recent developments in quantum hardware, which are not only addressing these challenges but also generating new prospects: • Addressing Challenges: Increased qubit counts and improved qubit quality empower quantum computers to tackle complex real-world problems, with reduced noise and enhanced scalability. • Contributing to Opportunities: Recent hardware advancements have catalyzed the creation of efficient quantum algorithms and the exploration of novel quantum applications in healthcare, finance, and materials science. • Creating Opportunities: Quantum computing, with its unique capabilities, has the potential to unlock scientific and engineering discoveries unattainable through classical computing, ushering in a new era of innovation across diverse industries. As quantum hardware continues to advance, these developments are poised to shape the future of quantum machine learning and its profound impact on society.
Quantum Machine Learning (QML) is an advanced discipline that emerges from the combined power of machine learning and quantum computing that has the ability to address intricate challenges in several domains. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. This research delves into the fundamental principles of quantum mechanics and their crucial role in quantum computing, emphasizing the potential of various quantum algorithms to surpass classical algorithms in specific computational tasks, and then methodically navigates through the quantum machine learning algorithms, offering profound insights into their application potential in revolutionizing data analysis and complex problem-solving methodologies, including their importance in the Language Learning Models (LLM) and Language Analysis Models (LAM). The study also provides insights into the various quantum platforms, encompassing both hardware and software aspects for the implementation of QML algorithms, and also explores the challenges prevalent in QML, with a particular focus on the limitations imposed by existing quantum hardware and the intricate nuances of data processing within quantum frameworks. This study contributes by presenting the basis for future research work related to the development of algorithms in the field of quantum machine learning and anticipating the far-reaching impact of QML across diverse scientific and technological domains.