×

Quantum systems for Monte Carlo methods and applications to fractional stochastic processes. (English) Zbl 07570634

Summary: Random numbers are a fundamental and useful resource in science and engineering with important applications in simulation, machine learning and cyber-security. Quantum systems can produce true random numbers because of the inherent randomness at the core of quantum mechanics. As a consequence, quantum random number generators are an efficient method to generate random numbers on a large scale. We study in this paper the applications of a viable source of unbiased quantum random numbers (QRNs) whose statistical properties can be arbitrarily programmed without the need for any post-processing and that pass all standard randomness tests of the NIST and Dieharder test suites without any randomness extraction. Our method is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. The advantages of our QRNs are shown via two applications: simulation of a fractional Brownian motion, which is a non-Markovian process, and option pricing under the fractional SABR model where the stochastic volatility process is assumed to be driven by a fractional Brownian motion. The results indicate that using the same number of random units, our QRNs achieve greater accuracy than those produced by standard pseudo-random number generators. Moreover, we demonstrate the advantages of our method via an increase in computational speed, efficiency, and convergence.

MSC:

82-XX Statistical mechanics, structure of matter

References:

[1] Herrero-Collantes, Miguel; Garcia-Escartin, Juan Carlos, Quantum random number generators, Rev. Modern Phys., 89, 1, 015004 (2017)
[2] Ma, Xiongfeng; Yuan, Xiao; Cao, Zhu; Qi, Bing; Zhang, Zhen, Quantum random number generation, npj Quantum Inf., 2, 16021 (2016)
[3] Zhang, Qiang; Deng, Xiaowei; Tian, Caixing; Su, Xiaolong, Quantum random number generator based on twin beams, Opt. Lett., 42, 5, 895-898 (2017)
[4] Nguyen, Lac; Rehain, Patrick; Sua, Yong Meng; Huang, Yu-Ping, Programmable quantum random number generator without postprocessing, Opt. Lett., 43, 4, 631-634 (2018)
[5] Glasserman, Paul, Monte Carlo Methods in Financial Engineering, Vol. 53 (2013), Springer Science & Business Media
[6] Flandrin, Patrick, Wavelet analysis and synthesis of fractional brownian motion, IEEE Trans. Inf. Theory, 38, 2, 910-917 (1992) · Zbl 0743.60078
[7] Chen, C.-C.; DaPonte, John S.; Fox, Martin D., Fractal feature analysis and classification in medical imaging, IEEE Trans. Med. Imaging, 8, 2, 133-142 (1989)
[8] Norros, Ilkka, On the use of fractional brownian motion in the theory of connectionless networks, IEEE J. Sel. Areas Commun., 13, 6, 953-962 (1995)
[9] Gatheral, Jim; Jaisson, Thibault; Rosenbaum, Mathieu, Volatility is rough, Quant. Finance, 1-17 (2018) · Zbl 1400.91590
[10] Kurtsiefer, Christian; Zarda, P.; Halder, Matthus; Weinfurter, H.; Gorman, P. M.; Tapster, P. R.; Rarity, J. G., Quantum cryptography: A step towards global key distribution, Nature, 419, 6906, 450 (2002)
[11] Acín, Antonio; Massar, Serge; Pironio, Stefano, Randomness versus nonlocality and entanglement, Phys. Rev. Lett., 108, 10, 100402 (2012)
[12] Click, Timothy H.; Liu, Aibing; Kaminski, George A., Quality of random number generators significantly affects results of Monte Carlo simulations for organic and biological systems, J. Comput. Chem., 32, 3, 513-524 (2011)
[13] Rukhin, Andrew; Soto, Juan; Nechvatal, James; Smid, Miles; Barker, Elaine, A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications (2001), Booz-Allen and Hamilton Inc: Booz-Allen and Hamilton Inc Mclean Va
[14] Kolmogorov, Andrei N., Wienersche spiralen und einige andere interessante kurven in Hilbertscen raum, CR (doklady), Acad. Sci. URSS (NS), 26, 115-118 (1940) · Zbl 0022.36001
[15] Mandelbrot, Benoit B.; Van Ness, John W., Fractional brownian motions, fractional noises and applications, SIAM Rev., 10, 4, 422-437 (1968) · Zbl 0179.47801
[16] Samoradnitsky, Gennady, Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance (2017), Routledge
[17] Nualart, David, The Malliavin Calculus and Related Topics, Vol. 1995 (2006), Springer · Zbl 1099.60003
[18] Alos, Elisa; Mazet, Olivier; Nualart, David, Stochastic calculus with respect to Gaussian processes, Ann. Probab., 766-801 (2001) · Zbl 1015.60047
[19] Zähle, Martine, Integration with respect to fractal functions and stochastic calculus. i, Probab. Theory Related Fields, 111, 3, 333-374 (1998) · Zbl 0918.60037
[20] Dieker, Ton, Simulation of Fractional Brownian Motion (2004), University of Twente: University of Twente Amsterdam, The Netherlands, (M.Sc. theses)
[21] Davies, Robert B.; Harte, D. S., Tests for Hurst effect, Biometrika, 74, 1, 95-101 (1987) · Zbl 0612.62123
[22] Bennedsen, Mikkel; Lunde, Asger; Pakkanen, Mikko S., Hybrid scheme for brownian semistationary processes, Finance Stoch., 21, 4, 931-965 (2017) · Zbl 1385.65010
[23] Ryan McCrickerd, Mikko S. Pakkanen, Turbocharging Monte Carlo pricing for the rough Bergomi model, 2017. arXiv preprint arXiv:1708.02563. · Zbl 1406.91486
[24] Beran, Jan, Statistics for Long-Memory Processes, Vol. 61 (1994), CRC press · Zbl 0869.60045
[25] Matsumoto, Makoto; Nishimura, Takuji, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Trans. Model. Comput. Simul. (TOMACS), 8, 1, 3-30 (1998) · Zbl 0917.65005
[26] Broadie, Mark; Jain, Ashish, Pricing and hedging volatility derivatives, J. Deriv., 15, 3, 7-24 (2008) · Zbl 1180.91283
[27] Jiro Akahori, Xiaoming Song, Tai-Ho Wang, Probability density of lognormal fractional sabr model, 2017. arXiv preprint arXiv:1702.08081.
[28] Alos, Elisa; Chatterjee, Rupak; Tudor, Sebastian F.; Wang, Tai-Ho, Target volatility option pricing in the lognormal fractional SABR model, Quantitative Finance, 19, 8, 1339-1356 (2019) · Zbl 1420.91441
[29] Chatterjee, Rupak, Practical Methods of Financial Engineering and Risk Management (2014), Springer-Apress
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.