2000 character limit reached
Deep learning-based variational autoencoder for classification of quantum and classical states of light (2405.05243v1)
Published 8 May 2024 in quant-ph, cs.LG, and physics.comp-ph
Abstract: Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements often requires efficient detectors and longer measurement times to obtain high-quality photon statistics. Here we introduce a deep learning-based variational autoencoder (VAE) method for classifying single photon added coherent state (SPACS), single photon added thermal state (SPACS), mixed states between coherent/SPACS and thermal/SPATS of light. Our semisupervised learning-based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non-unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. We envision that such a deep learning methodology will enable better classification of quantum light and light sources even in the presence of poor detection quality.
- Christopher Monroe and Jungsang Kim, “Scaling the ion trap quantum processor,” Science 339, 1164–1169 (2013).
- H Jeff Kimble, “The quantum internet,” Nature 453, 1023–1030 (2008).
- Hyukjoon Kwon, Kok Chuan Tan, Tyler Volkoff, and Hyunseok Jeong, “Nonclassicality as a quantifiable resource for quantum metrology,” Physical review letters 122, 040503 (2019).
- P Van Loock, TD Ladd, K Sanaka, F Yamaguchi, Kae Nemoto, WJ Munro, and Y Yamamoto, “Hybrid quantum repeater using bright coherent light,” Physical review letters 96, 240501 (2006).
- Fulvio Flamini, Nicolo Spagnolo, and Fabio Sciarrino, “Photonic quantum information processing: a review,” Reports on Progress in Physics 82, 016001 (2018).
- Paul-Antoine Moreau, Ermes Toninelli, Thomas Gregory, and Miles J Padgett, “Imaging with quantum states of light,” Nature Reviews Physics 1, 367–380 (2019).
- Dietrich Leibfried, Murray D Barrett, T Schaetz, Joseph Britton, J Chiaverini, Wayne M Itano, John D Jost, Christopher Langer, and David J Wineland, “Toward heisenberg-limited spectroscopy with multiparticle entangled states,” Science 304, 1476–1478 (2004).
- Jonathan P Dowling, “Quantum optical metrology–the lowdown on high-n00n states,” Contemporary physics 49, 125–143 (2008).
- Christian L Degen, Friedemann Reinhard, and Paola Cappellaro, “Quantum sensing,” Reviews of modern physics 89, 035002 (2017).
- Ilan Kremer, Quantum communication (Citeseer, 1995).
- Michael A Nielsen and Isaac Chuang, “Quantum computation and quantum information,” (2002).
- Nicolas Gisin and Rob Thew, “Quantum communication,” Nature photonics 1, 165–171 (2007).
- Takafumi Ono, Ryo Okamoto, and Shigeki Takeuchi, “An entanglement-enhanced microscope,” Nature communications 4, 2426 (2013).
- Gabriela Barreto Lemos, Victoria Borish, Garrett D Cole, Sven Ramelow, Radek Lapkiewicz, and Anton Zeilinger, “Quantum imaging with undetected photons,” Nature 512, 409–412 (2014).
- GS Agarwal and K Tara, “Nonclassical properties of states generated by the excitations on a coherent state,” Physical Review A 43, 492 (1991).
- GS Agarwal and K Tara, “Nonclassical character of states exhibiting no squeezing or sub-poissonian statistics,” Physical Review A 46, 485 (1992).
- Roman Schnabel, “Squeezed states of light and their applications in laser interferometers,” Physics Reports 684, 1–51 (2017).
- Daniel Braun, Pu Jian, Olivier Pinel, and Nicolas Treps, “Precision measurements with photon-subtracted or photon-added gaussian states,” Physical Review A 90, 013821 (2014).
- Gilles Van Assche, Quantum cryptography and secret-key distillation (Cambridge University Press, 2006).
- Stephen M Barnett, Gergely Ferenczi, Claire R Gilson, and Fiona C Speirits, “Statistics of photon-subtracted and photon-added states,” Physical Review A 98, 013809 (2018).
- Valentina Parigi, Alessandro Zavatta, Myungshik Kim, and Marco Bellini, “Probing quantum commutation rules by addition and subtraction of single photons to/from a light field,” Science 317, 1890–1893 (2007).
- Kosmas L Tsakmakidis, Pankaj K Jha, Yuan Wang, and Xiang Zhang, “Quantum coherence–driven self-organized criticality and nonequilibrium light localization,” Science advances 4, eaaq0465 (2018).
- Kaveh Najafian, Ziv Meir, Mudit Sinhal, and Stefan Willitsch, “Identification of molecular quantum states using phase-sensitive forces,” Nature communications 11, 4470 (2020).
- Abhishek Mall, Abhijeet Patil, Dipesh Tamboli, Amit Sethi, and Anshuman Kumar, “Fast design of plasmonic metasurfaces enabled by deep learning,” Journal of Physics D: Applied Physics 53, 49LT01 (2020a).
- Abhishek Mall, Abhijeet Patil, Amit Sethi, and Anshuman Kumar, “A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces,” Scientific reports 10, 1–12 (2020b).
- Peter R Wiecha, Arnaud Arbouet, Christian Girard, and Otto L Muskens, “Deep learning in nano-photonics: inverse design and beyond,” Photonics Research 9, B182–B200 (2021).
- Davide Piccinotti, Kevin F MacDonald, Simon A Gregory, Ian Youngs, and Nikolay I Zheludev, “Artificial intelligence for photonics and photonic materials,” Reports on Progress in Physics 84, 012401 (2020).
- Kai Qu, Ke Chen, Qi Hu, Junming Zhao, Tian Jiang, and Yijun Feng, “Deep-learning-assisted inverse design of dual-spin/frequency metasurface for quad-channel off-axis vortices multiplexing,” Advanced Photonics Nexus 2, 016010 (2023).
- Mohammadreza Zandehshahvar, Yashar Kiarashinejad, Muliang Zhu, Hossein Maleki, Tyler Brown, and Ali Adibi, “Manifold learning for knowledge discovery and intelligent inverse design of photonic nanostructures: breaking the geometric complexity,” ACS Photonics 9, 714–721 (2022).
- Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature 567, 209–212 (2019).
- Siddhant Garg and Goutham Ramakrishnan, “Advances in quantum deep learning: An overview,” arXiv preprint arXiv:2005.04316 (2020).
- Chenglong You, Mario A Quiroz-Juárez, Aidan Lambert, Narayan Bhusal, Chao Dong, Armando Perez-Leija, Amir Javaid, Roberto de J León-Montiel, and Omar S Magaña-Loaiza, “Identification of light sources using machine learning,” Applied Physics Reviews 7, 021404 (2020).
- Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, and Anton Frisk Kockum, “Classification and reconstruction of optical quantum states with deep neural networks,” Physical Review Research 3, 033278 (2021).
- Zhaxylyk A Kudyshev, Simeon I Bogdanov, Theodor Isacsson, Alexander V Kildishev, Alexandra Boltasseva, and Vladimir M Shalaev, “Rapid classification of quantum sources enabled by machine learning,” Advanced Quantum Technologies 3, 2000067 (2020).
- Valentin Gebhart, Martin Bohmann, Karsten Weiher, Nicola Biagi, Alessandro Zavatta, Marco Bellini, and Elizabeth Agudelo, “Identifying nonclassicality from experimental data using artificial neural networks,” Physical Review Research 3, 023229 (2021).
- Valentin Gebhart and Martin Bohmann, “Neural-network approach for identifying nonclassicality from click-counting data,” Physical Review Research 2, 023150 (2020).
- Su-Yong Lee and Hyunchul Nha, “Quantum state engineering by a coherent superposition of photon subtraction and addition,” Physical Review A 82, 053812 (2010).
- Shuai Wang, Hong-yi Fan, and Li-yun Hu, “Photon-number distributions of non-gaussian states generated by photon subtraction and addition,” JOSA B 29, 1020–1028 (2012).
- Jie Li, Simon Gröblacher, Shi-Yao Zhu, and GS Agarwal, “Generation and detection of non-gaussian phonon-added coherent states in optomechanical systems,” Physical Review A 98, 011801 (2018).
- ET Burch, C Henelsmith, W Larson, and M Beck, “Quantum-state tomography of single-photon entangled states,” Physical Review A 92, 032328 (2015).
- Marco Bellini, Hyukjoon Kwon, Nicola Biagi, Saverio Francesconi, Alessandro Zavatta, and MS Kim, “Demonstrating quantum microscopic reversibility using coherent states of light,” Physical Review Letters 129, 170604 (2022).
- Vittorio Giovannetti, Seth Lloyd, and Lorenzo Maccone, “Quantum-enhanced measurements: beating the standard quantum limit,” Science 306, 1330–1336 (2004).
- Girish S. Agarwal, Quantum Optics (Cambridge University Press, 2012).
- Romain Alléaume, Francois Treussart, Jean-Michel Courty, and Jean-Francois Roch, “Photon statistics characterization of a single-photon source,” New Journal of physics 6, 85 (2004).
- Diederik P Kingma and Max Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv 1312.6114 (2013).
- Laurens Van der Maaten and Geoffrey Hinton, “Visualizing data using t-sne.” Journal of machine learning research 9 (2008).
- Hamidreza Akbari, Wei-Hsiang Lin, Benjamin Vest, Pankaj K Jha, and Harry A Atwater, “Temperature-dependent spectral emission of hexagonal boron nitride quantum emitters on conductive and dielectric substrates,” Physical Review Applied 15, 014036 (2021).
- Pankaj K Jha, Hamidreza Akbari, Yonghwi Kim, Souvik Biswas, and Harry A Atwater, “Nanoscale axial position and orientation measurement of hexagonal boron nitride quantum emitters using a tunable nanophotonic environment,” Nanotechnology 33, 015001 (2021).
- Hamidreza Akbari, Souvik Biswas, Pankaj Kumar Jha, Joeson Wong, Benjamin Vest, and Harry A Atwater, “Lifetime-limited and tunable quantum light emission in h-bn via electric field modulation,” Nano Letters 22, 7798–7803 (2022).
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.