QuantuMoonLight: A low-code platform to experiment with quantum machine learning

Amato, Francesco; Cicalese, Matteo; Contrasto, Luca; Cubicciotti, Giacomo; D’Ambola, Gerardo; Marca, Antonio La; Pagano, Giuseppe; Tomeo, Fiorentino; Robertazzi, Gennaro Alessio; Vassallo, Gabriele; Acampora, Giovanni; Vitiello, Autilia; Catolino, Gemma; Giordano, Giammaria; Lambiase, Stefano; Pontillo, Valeria; Sellitto, Giulia; Ferrucci, Filomena; Palomba, Fabio

doi:10.1016/j.softx.2023.101399

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Building on this perspective, Amato et al (2023) present 'QuantuMoonLight,' a user-friendly, low-code platform designed to streamline quantum machine learning experiments. This platform facilitates the comparison of quantum algorithms with classical ones, making quantum technologies more accessible and encouraging community collaboration.…”

Section: Future Trends and Research Directionsmentioning

confidence: 99%

Machine Learning Applications of Quantum Computing: A Review

Nguyen,

Sipola,

Hautamäki

2024

eccws

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At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.

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Section: Future Trends and Research Directionsmentioning

confidence: 99%