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Quantum learning advantage on a scalable photonic platform.

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Title: Quantum learning advantage on a scalable photonic platform.
Authors: Liu, Zheng-Hao, Brunel, Romain, Østergaard, Emil E. B., Cordero, Oscar, Chen, Senrui, Wong, Yat, Nielsen, Jens A. H., Bregnsbo, Axel B., Zhou, Sisi, Huang, Hsin-Yuan, Oh, Changhun, Jiang, Liang, Preskill, John, Neergaard-Nielsen, Jonas S., Andersen, Ulrik L.
Source: Science. 9/25/2025, Vol. 389 Issue 6767, p1332-1335. 4p.
Subjects: Quantum mechanics, Photonics, Machine learning, Computer network protocols, Magnitude (Mathematics)
Abstract: Recent advances in quantum technologies have demonstrated that quantum systems can outperform classical ones in specific tasks, a concept known as quantum advantage. Although previous efforts have focused on computational speedups, a definitive and provable quantum advantage that is unattainable by any classical system has remained elusive. In this work, we demonstrate a provable photonic quantum advantage by implementing a quantum-enhanced protocol for learning a high-dimensional physical process. Using imperfect Einstein–Podolsky–Rosen entanglement, we achieve a sample complexity reduction of 11.8 orders of magnitude compared to classical methods without entanglement. These results show that large-scale, provable quantum advantage is achievable with current photonic technology and represent a key step toward practical quantum-enhanced learning protocols in quantum metrology and machine learning. Editor's summary: Understanding the properties of a system requires making measurements of a variable and then using that information to piece together a detailed understanding. As the system becomes more complex, the number of required measurements increases exponentially, placing fundamental limits on obtaining that information using classical means. Liu et al. demonstrated a quantum learning advantage in an optical system using entangled photons as probes to reduce the sampling complexity of the process by more than 11 orders of magnitude compared with classical probes. This result highlights a route to quantum enhanced learning protocols for machine learning and developing quantum technologies. — Ian S. Osborne [ABSTRACT FROM AUTHOR]
Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Quantum learning advantage on a scalable photonic platform.
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  Data: <searchLink fieldCode="JN" term="%22Science%22">Science</searchLink>. 9/25/2025, Vol. 389 Issue 6767, p1332-1335. 4p.
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  Data: Recent advances in quantum technologies have demonstrated that quantum systems can outperform classical ones in specific tasks, a concept known as quantum advantage. Although previous efforts have focused on computational speedups, a definitive and provable quantum advantage that is unattainable by any classical system has remained elusive. In this work, we demonstrate a provable photonic quantum advantage by implementing a quantum-enhanced protocol for learning a high-dimensional physical process. Using imperfect Einstein–Podolsky–Rosen entanglement, we achieve a sample complexity reduction of 11.8 orders of magnitude compared to classical methods without entanglement. These results show that large-scale, provable quantum advantage is achievable with current photonic technology and represent a key step toward practical quantum-enhanced learning protocols in quantum metrology and machine learning. Editor's summary: Understanding the properties of a system requires making measurements of a variable and then using that information to piece together a detailed understanding. As the system becomes more complex, the number of required measurements increases exponentially, placing fundamental limits on obtaining that information using classical means. Liu et al. demonstrated a quantum learning advantage in an optical system using entangled photons as probes to reduce the sampling complexity of the process by more than 11 orders of magnitude compared with classical probes. This result highlights a route to quantum enhanced learning protocols for machine learning and developing quantum technologies. — Ian S. Osborne [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=188243862
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