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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 , Author Presentation

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
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    [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).

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    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," Photonics Res. 11, 1703 (2023)

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    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