Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Photonics Research, Volume. 11, Issue 10, 1703(2023)

Efficient option pricing with a unary-based photonic computing chip and generative adversarial learning On the Cover

Hui Zhang1,2, Lingxiao Wan2, Sergi Ramos-Calderer3,4, Yuancheng Zhan2, Wai-Keong Mok5, Hong Cai6, Feng Gao7, Xianshu Luo7, Guo-Qiang Lo7, Leong Chuan Kwek2,5,8, José Ignacio Latorre3,4,5, and Ai Qun Liu1,2、*
Author Affiliations
  • 1Institute of Quantum Technologies (IQT), The Hong Kong Polytechnic University, Hong Kong, China
  • 2Quantum Science and Engineering Centre (QSec), Nanyang Technological University, Singapore, Singapore
  • 3Departament de Fisica Quantica i Astrofisica and Institut de Ciencies del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain
  • 4Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE
  • 5Centre for Quantum Technologies, National University of Singapore, Singapore, Singapore
  • 6Institute of Microelectronics, A*STAR (Agency for Science, Technology and Research), Singapore, Singapore
  • 7Advanced Micro Foundry, Singapore, Singapore
  • 8National Institute of Education, Nanyang Technological University, Singapore, Singapore
    • show less
    Full TextGet PDF
    Figures&Tables (8)
    Equations (22)
    References (40)
    Cited By (3)
    Tools
    Paper Information
    Topics
    References(40)

    [1] F. Black, M. Scholes. The pricing of options and corporate liabilities. Foundations of CCA and Equity Valuation, 3-21(2019).

    [2] R. Orus, S. Mugel, E. Lizaso. Quantum computing for finance: overview and prospects. Rev. Phys., 4, 100028(2019).

    [3] D. Herman, C. Googin, X. Liu, A. Galda, I. Safro, Y. Sun, M. Pistoia, Y. Alexeev. A survey of quantum computing for finance. arXiv(2022).

    [4] G. Brassard, P. Hoyer, M. Mosca, A. Tapp. Quantum amplitude amplification and estimation. arXiv(2002).

    [5] A. Montanaro. Quantum speedup of Monte Carlo methods. Proc. R. Soc. A, 471, 20150301(2015).

    [6] P. Rebentrost, B. Gupt, T. R. Bromley. Quantum computational finance: Monte Carlo pricing of financial derivatives. Phys. Rev. A, 98, 022321(2018).

    [7] S. Woerner, D. J. Egger. Quantum risk analysis. npj Quantum Inf., 5, 15(2019).

    [8] S. Focardi, F. J. Fabozzi, D. Mazza. Quantum option pricing and quantum finance. J. Deriv., 28, 79-98(2020).

    [9] S. Ramos-Calderer, A. Pérez-Salinas, D. Garca-Martn, C. Bravo-Prieto, J. Cortada, J. Planaguma, J. I. Latorre. Quantum unary approach to option pricing. Phys. Rev. A, 103, 032414(2021).

    [10] F. Bova, A. Goldfarb, R. G. Melko. Commercial applications of quantum computing. EPJ Quantum Technol., 8, 2(2021).

    [11] N. Stamatopoulos, D. J. Egger, Y. Sun, C. Zoufal, R. Iten, N. Shen, S. Woerner. Option pricing using quantum computers. Quantum, 4, 291(2020).

    [12] J. Preskill. Quantum computing in the NISQ era and beyond. Quantum, 2, 79(2018).

    [13] K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, A. Aspuru-Guzik. Noisy intermediate-scale quantum algorithms. Rev. Mod. Phys., 94, 015004(2022).

    [14] H. Zhang, L. Wan, T. Haug, W.-K. Mok, S. Paesani, Y. Shi, H. Cai, L. K. Chin, M. F. Karim, L. Xiao, X. Luo, F. Gao, B. Dong, S. Assad, M. S. Kim, A. Laing, L. C. Kwek, A. Q. Liu. Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder. Sci. Adv., 8, eabn9783(2022).

    [15] T. Wang, M. M. Sohoni, L. G. Wright, M. M. Stein, S.-Y. Ma, T. Onodera, M. G. Anderson, P. L. McMahon. Image sensing with multilayer nonlinear optical neural networks. Nat. Photonics, 17, 408-415(2023).

    [16] T. Fu, Y. Zang, Y. Huang, Z. Du, H. Huang, C. Hu, M. Chen, S. Yang, H. Chen. Photonic machine learning with on-chip diffractive optics. Nat. Commun., 14, 70(2023).

    [17] S. Xu, J. Wang, H. Shu, Z. Zhang, S. Yi, B. Bai, X. Wang, J. Liu, W. Zou. Optical coherent dot-product chip for sophisticated deep learning regression. Light Sci. Appl., 10, 221(2021).

    [18] K. Liao, Y. Chen, Z. Yu, X. Hu, X. Wang, C. Lu, H. Lin, Q. Du, J. Hu, Q. Gong. All-optical computing based on convolutional neural networks. Opto-Electron. Adv., 4, 200060(2021).

    [19] H. Zhou, J. Dong, J. Cheng, W. Dong, C. Huang, Y. Shen, Q. Zhang, M. Gu, C. Qian, H. Chen, Z. Ruan, X. Zhang. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl., 11, 30(2022).

    [20] J. Romero, A. Aspuru-Guzik. Variational quantum generators: generative adversarial quantum machine learning for continuous distributions. Adv. Quantum Technol., 4, 2000003(2021).

    [21] H. Zhang, M. Gu, X. Jiang, J. Thompson, H. Cai, S. Paesani, R. Santagati, A. Laing, Y. Zhang, M. Yung, Y. Z. Shi, F. K. Muhammad, G. Q. Lo, X. S. Luo, B. Dong, D. L. Kwong, L. C. Kwek, A. Q. Liu. An optical neural chip for implementing complex-valued neural network. Nat. Commun., 12, 457(2021).

    [22] Q. Zhang, H. Yu, M. Barbiero, B. Wang, M. Gu. Artificial neural networks enabled by nanophotonics. Light Sci. Appl., 8, 42(2019).

    [23] J. Spall, X. Guo, T. D. Barrett, A. I. Lvovsky. Fully reconfigurable coherent optical vector–matrix multiplication. Opt. Lett., 45, 5752-5755(2020).

    [24] S. Yan, J. Dong. Metasurface for highly-efficient on-chip classical and quantum all-optical modulation. Light Sci. Appl., 11, 238(2022).

    [25] C. Zoufal, A. Lucchi, S. Woerner. Quantum generative adversarial networks for learning and loading random distributions. npj Quantum Inf., 5, 103(2019).

    [26] L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, L. Sun. Quantum generative adversarial learning in a superconducting quantum circuit. Sci. Adv., 5, eaav2761(2019).

    [27] H.-L. Huang, Y. Du, M. Gong, Y. Zhao, Y. Wu, C. Wang, S. Li, F. Liang, J. Lin, Y. Xu, R. Yang, T. Liu, M.-H. Hsieh, H. Deng, H. Rong, C.-Z. Peng, C.-Y. Lu, Y.-A. Chen, D. Tao, X. Zhu, J.-W. Pan. Experimental quantum generative adversarial networks for image generation. Phys. Rev. Appl., 16, 024051(2021).

    [28] K. Huang, Z.-A. Wang, C. Song, K. Xu, H. Li, Z. Wang, Q. Guo, Z. Song, Z.-B. Liu, D. Zheng, H. Wang, J.-G. Tian, H. Fan. Quantum generative adversarial networks with multiple superconducting qubits. npj Quantum Inf., 7, 165(2021).

    [29] C. Wu, X. Yang, H. Yu, R. Peng, I. Takeuchi, Y. Chen, M. Li. Harnessing optoelectronic noises in a photonic generative network. Sci. Adv., 8, eabm2956(2022).

    [30] S. Lloyd, C. Weedbrook. Quantum generative adversarial learning. Phys. Rev. Lett., 121, 040502(2018).

    [31] Y. Suzuki, S. Uno, R. Raymond, T. Tanaka, T. Onodera, N. Yamamoto. Amplitude estimation without phase estimation. Quantum Inf. Process., 19, 75(2020).

    [32] S. Aaronson, P. Rall. Quantum approximate counting, simplified. Symposium on Simplicity in Algorithms, 24-32(2020).

    [33] D. Grinko, J. Gacon, C. Zoufal, S. Woerner. Iterative quantum amplitude estimation. npj Quantum Inf., 7, 52(2021).

    [34] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio. Generative adversarial networks. Advances in Neural Information Processing Systems, 27, 2672-2680(2014).

    [35] M. Arjovsky, S. Chintala, L. Bottou. Wasserstein generative adversarial networks. International Conference on Machine Learning, 214-223(2017).

    [36] S. Chakrabarti, H. Yiming, T. Li, S. Feizi, X. Wu. Quantum Wasserstein generative adversarial networks. arXiv(2019).

    [37] S. Lloyd. Quantum search without entanglement. Phys. Rev. A, 61, 010301(1999).

    [38] D. A. Meyer. Sophisticated quantum search without entanglement. Phys. Rev. Lett., 85, 2014-2017(2000).

    [39] A. Ekert, R. Jozsa. Quantum algorithms: entanglement–enhanced information processing. Philos. Trans. R. Soc., 356, 1769-1782(1998).

    [40] S. Wallis. Binomial confidence intervals and contingency tests: mathematical fundamentals and the evaluation of alternative methods. J. Quant. Linguist., 20, 178-208(2013).

    Tools

    Get Citation

    Copy Citation Text

    Hui Zhang, Lingxiao Wan, Sergi Ramos-Calderer, Yuancheng Zhan, Wai-Keong Mok, Hong Cai, Feng Gao, Xianshu Luo, Guo-Qiang Lo, Leong Chuan Kwek, José Ignacio Latorre, Ai Qun Liu. Efficient option pricing with a unary-based photonic computing chip and generative adversarial learning[J]. Photonics Research, 2023, 11(10): 1703

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Integrated Optics

    Received: Apr. 25, 2023

    Accepted: Jul. 31, 2023

    Published Online: Sep. 27, 2023

    The Author Email: Ai Qun Liu (aiqun.liu@polyu.edu.hk)

    DOI:10.1364/PRJ.493865

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology