What is it about?
We explore how quantum computing can enhance machine learning. The paper presents the main methods, the challenges that remain, and promising applications in science and industry, offering a practical guide for researchers and practitioners.
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Why is it important?
It is important because quantum machine learning sits at the intersection of two transformative fields. Quantum computing promises computational advantages beyond classical methods, while machine learning is central to modern science and technology. Together, they may open new ways to address problems in optimization, chemistry, materials, and data analysis that are currently out of reach. Mapping the state of the art helps researchers focus on what works today, identify obstacles that still need to be solved, and connect theory with practical applications. This guidance is crucial for shaping future progress in both research and industry.
Perspectives
Quantum machine learning is still in its early stages, but steady progress is being made in algorithms, hardware, and practical demonstrations. In the near term, hybrid methods that combine classical and quantum techniques are likely to be the most effective. In the longer run, advances in quantum devices may allow applications that go beyond the reach of current technology, with potential impact on areas such as drug discovery, logistics, and materials science. Clear benchmarks, realistic expectations, and continued collaboration between communities will be essential to turn today’s concepts into tomorrow’s solutions.
Francisco Martinez Álvarez
Universidad Pablo de Olavide
Read the Original
This page is a summary of: A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications, ACM Computing Surveys, September 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3764582.
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