Quantum Photonics and AI Synergy: Advancements in Optical Metrology, Sensing, and Communication
Keywords:
N\AAbstract
The convergence of quantum photonics and artificial intelligence (AI) is redefining the landscape of optical metrology, sensing, and communication, enabling unprecedented precision, adaptability, and data processing capabilities. This paper explores the synergistic integration of AI-driven algorithms with quantum photonic systems, highlighting transformative advancements such as machine learning-enhanced quantum state estimation, intelligent control of photonic circuits, and adaptive quantum error correction. Emphasis is placed on how AI facilitates real-time decision-making and noise mitigation in complex quantum environments, thereby enhancing the sensitivity and resolution of optical sensors and metrological instruments. Additionally, the study investigates AI-assisted quantum communication protocols that optimize entanglement distribution, secure key generation, and photonic resource management. By bridging theoretical insights with emerging experimental frameworks, this work presents a comprehensive perspective on the mutual reinforcement between quantum photonics and AI, outlining their collective potential to drive the next generation of ultra-precise, intelligent optical technologies.
Downloads
Metrics
References
Pieter Kok, William J. Munro, Kae Nemoto, Timothy C. Ralph, Jonathan P. Dowling, and Gerald J. Milburn. (2007). Linear optical quantum computing with photonic qubits. Reviews of Modern Physics, 79(1), 135–174.
Scott Aaronson and Alex Arkhipov. (2011). The computational complexity of linear optics. In Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC), 333–342.
Francesco Flamini, Nicolò Spagnolo, and Fabio Sciarrino. (2018). Photonic quantum information processing: A review. Reports on Progress in Physics, 82(1), 016001.
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002.
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. (2017). Quantum machine learning. Nature, 549, 195–202.
Alexander Hentschel and Barry C. Sanders. (2010). Machine learning for precise quantum measurement. Physical Review Letters, 104(6), 063603.
Andrea Lumino, Enrico Polino, Alessandro S. Rab, Giuseppe Milani, Nicolò Spagnolo, and Fabio Sciarrino. (2018). Experimental phase estimation enhanced by machine learning. Physical Review Applied, 10(4), 044033.
Davood Shiri, Hing Yuen, and Hamid R. Rabiee. (2020). Artificial intelligence meets quantum communications: A comprehensive survey. ACM Computing Surveys (CSUR), 54(6), 1–39.
Abhishek Sharma, Rakesh Dash, and Anirban Pathak. (2022). Quantum machine learning-enhanced quantum key distribution: A futuristic perspective. Quantum Information Processing, 21, 48.
Zihan Zhang, Ronald J. Sadlier, and Lin You. (2020). Deep reinforcement learning for adaptive quantum error correction. Physical Review Research, 2(2), 023020.
Joseph F. Fitzsimons and Elham Kashefi. (2017). Unconditionally verifiable blind quantum computation. Physical Review A, 96(1), 012303.
Ahmad W. Elshaari, Wolfram Pernice, Kartik Srinivasan, Oliver Benson, and Val Zwiller. (2020). Hybrid integrated quantum photonic circuits. Nature Photonics, 14, 285–298.
Feihu Xu, Xuedong Liu, Jun Xu, Li Zhang, and Liang Wang. (2019). Quantum photonic entanglement distribution in scalable networks: A review. Quantum Information Processing, 18(6), 202.
Frank Arute, Kunal Arya, Patrick Bent, et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574, 505–510.
Benjamin P. Lanyon, Jonathan T. Barreiro, Timothy C. Wei, et al. (2008). Experimental demonstration of a robust, high-fidelity geometric two-qubit phase gate. Nature, 459(7246), 728–732.
Ion M. Georgescu, Shahan Ashhab, and Franco Nori. (2014). Quantum simulation. Reviews of Modern Physics, 86(1), 153–185.
L. M. O'Brien, Akira Furusawa, and Jelena Vučković. (2009). Photonic quantum technologies. Nature Photonics, 3(12), 687–695.
Stefano Pirandola, Roberto Laurenza, Chiara Ottaviani, and Leonardo Banchi. (2018). Fundamental limits of repeaterless quantum communications. Nature Communications, 9(1), 1–10.
Walter Tittel, Hugo Zbinden, and Nicolas Gisin. (2000). Experimental demonstration of quantum cryptography over 67 km of optical fibre. Physical Review A, 63(4), 042301.
Zhen Zhen Xie, Fang Zhang, Zhaozong Xu, and Wen Zou. (2020). Quantum machine learning for optimization problems in quantum computing. npj Quantum Information, 6(1), 10.
Yuan Liu, et al. (2020). Quantum-enhanced optical communication using single-photon detectors. Nature Communications, 11, 5159.
Xiaodong Zhang, Hong Zhang, Huan Zhang, and Qing Zhan. (2021). Quantum error correction and fault tolerance in quantum computing: Current status and future prospects. Quantum Science and Technology, 6(1), 014001.
Nicolas Gisin, Hugo Zbinden, and Walter Tittel. (2002). Quantum communication. Nature Photonics, 5, 250–256.
Lei Liu, Shuo Chen, and Xian Zhou. (2020). Advances in quantum machine learning with quantum photonics. Frontiers in Physics, 8, 168.
Nicolas Sangouard, Christoph Simon, Hugues de Riedmatten, and Nicolas Gisin. (2011). Quantum repeaters based on atomic ensembles and linear optics. Reviews of Modern Physics, 83(1), 33–80.
Marc-Antoine Lemonde, Nicolas Didier, and Alexandre Blais. (2016). Enhanced nonlinear interactions in quantum optomechanics via mechanical amplification. Nature Communications, 7, 11338.
David R. Hamel, Levi K. Shalm, Heather M. Choi, et al. (2014). Direct generation of three-photon polarization entanglement. Nature Photonics, 8(10), 801–807.
Thomas Monz, Daniel Nigg, Esteban A. Martinez, et al. (2016). Realization of a scalable Shor algorithm. Science, 351(6277), 1068–1070.
Mohammad Hafezi, Eugene A. Demler, Mikhail D. Lukin, and Jacob M. Taylor. (2011). Robust optical delay lines with topological protection. Nature Physics, 7(11), 907–912.
Christoph Marquardt and Gerd Leuchs. (2007). Quantum cryptography: Photons can keep secrets. Physics World, 20(3), 24–29.
Peter W. Shor. (1995). Scheme for reducing decoherence in quantum computer memory. Physical Review A, 52(4), R2493–R2496.
Frédéric Grosshans and Philippe Grangier. (2002). Continuous variable quantum cryptography using coherent states. Physical Review Letters, 88(5), 057902.
Yiwen Chu, Nathan R. Lee, Andrew J. Kollár, et al. (2018). Creation and control of multi-phonon Fock states in a bulk acoustic-wave resonator. Nature, 563(7733), 666–670.
Alexander Zeilinger, Anton Zeilinger, Jian-Wei Pan, and Markus Aspelmeyer. (2005). Experimental quantum teleportation. Nature, 430, 849–853.
Matthew D. Reid, Peter D. Drummond, Wolfgang P. Bowen, et al. (2009). Colloquium: The Einstein-Podolsky-Rosen paradox: From concepts to applications. Reviews of Modern Physics, 81(4), 1727–1751.
Andrew J. Shields. (2007). Semiconductor quantum light sources. Nature Photonics, 1(4), 215–223.
..
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
