1Department of Engineering Physics, École Polytechnique de Montréal, Montréal, QC, H3T 1JK, Canada
2Departamento de Física, Universidad Nacional de Colombia – Sede Bogotá, Facultad de Ciencias, Grupo de Óptica e Información Cuántica, Carrera 30 Calle 45-03, C.P. 111321, Bogotá, Colombia
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Abstract
Recently, Zhong et al. [1, 2] performed landmark Gaussian boson sampling experiments with up to 144 modes using threshold detectors. The authors claim to have achieved quantum computational advantage with the implementation of these experiments, named Jiuzhang 1.0 and Jiuzhang 2.0. Their experimental results are validated against several classical hypotheses and adversaries using tests such as the comparison of statistical correlations between modes, Bayesian hypothesis testing and the Heavy Output Generation (HOG) test. In this work we propose an alternative classical hypothesis for the validation of these experiments. We use the probability distribution of mixtures of coherent states sent into a lossy interferometer; these input mixed states, which we term $textit{squashed states}$, have vacuum fluctuations in one quadrature and excess fluctuations in the other. We find that for configurations in the high photon number density regime, the comparison of statistical correlations does not tell apart the ground truth of the experiment (two-mode squeezed states sent into an interferometer) from our alternative hypothesis. On the other hand, the Bayesian test indicates that, for all configurations excepting Jiuzhang 1.0, the ground truth is a more likely explanation of the experimental data than our alternative hypothesis. A similar result is obtained for the HOG test: for all configurations of Jiuzhang 2.0, the test indicates that the experimental samples have higher ground truth probability than the samples obtained from our alternative distribution; for Jiuzhang 1.0 the test is inconclusive. Our results provide a new hypothesis that should be considered in the validation of future GBS experiments, and shed light into the need to identify proper metrics to verify quantum advantage in the context of threshold GBS. Additionally, they indicate that a classical explanation of the Jiuzhang 1.0 experiment, lacking any quantum features, has not been ruled out.
Featured image: (Left) Description of the Jiuzhang 1.0/2.0 GBS hypotheses. Pairs of adjacent input squeezed or squashed states are sent into 50:50 beamsplitters represented by the vertical lines with dots at the end. The alternating orientation of the input states together with the 50:50 beamsplitters generate pairs of two-mode squeezed or squashed states. These states are then sent into a lossy interferometer, represented by the rectangle, whose action is described by a rectangular sub-unitary matrix $boldsymbol{T}$. The output state of the interferometer is sampled using threshold detectors. (Right) Representation of the noise ellipse of several Gaussian states used for the validation of the Jiuzhang 1.0/2.0 experiments. The ground truth of the experiments corresponds to the use of squeezed states corresponding to the blue line. Jiuzhang 1.0 and Jiuzhang 2.0 have been validated against hypotheses using coherent states and thermal states represented by the red-dotted and green-dotted lines, respectively. In this work we validate the experimental results against a hypothesis using the squashed states represented by the purple line. For reference, we also show the ellipse corresponding to the vacuum state with a black-dotted line.
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[1] Han-Sen Zhong, Hui Wang, Yu-Hao Deng, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Jian Qin, Dian Wu, Xing Ding, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Chao-Yang Lu, and Jian-Wei Pan. Quantum computational advantage using photons. Science, 370 (6523): 1460–1463, 2020a. https://doi.org/10.1126/science.abe8770.
https://doi.org/10.1126/science.abe8770
[2] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, Si-Qiu Gong, Hao Su, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Jelmer J. Renema, Chao-Yang Lu, and Jian-Wei Pan. Phase-programmable gaussian boson sampling using stimulated squeezed light. Phys. Rev. Lett., 127: 180502, 10 2021a. 10.1103/PhysRevLett.127.180502. URL https://link.aps.org/doi/10.1103/PhysRevLett.127.180502.
https://doi.org/10.1103/PhysRevLett.127.180502
[3] Aram W Harrow and Ashley Montanaro. Quantum computational supremacy. Nature, 549 (7671): 203–209, 2017. https://doi.org/10.1038/nature23458.
https://doi.org/10.1038/nature23458
[4] Dominik Hangleiter and Jens Eisert. Computational advantage of quantum random sampling. arXiv preprint arXiv:2206.04079, 2022. https://doi.org/10.48550/arXiv.2206.04079.
https://doi.org/10.48550/arXiv.2206.04079
arXiv:2206.04079
[5] Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al. Quantum supremacy using a programmable superconducting processor. Nature, 574 (7779): 505–510, 2019. https://doi.org/10.1038/s41586-019-1666-5.
https://doi.org/10.1038/s41586-019-1666-5
[6] Yulin Wu, Wan-Su Bao, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, Ming Gong, Cheng Guo, Chu Guo, Shaojun Guo, Lianchen Han, Linyin Hong, He-Liang Huang, Yong-Heng Huo, Liping Li, Na Li, Shaowei Li, Yuan Li, Futian Liang, Chun Lin, Jin Lin, Haoran Qian, Dan Qiao, Hao Rong, Hong Su, Lihua Sun, Liangyuan Wang, Shiyu Wang, Dachao Wu, Yu Xu, Kai Yan, Weifeng Yang, Yang Yang, Yangsen Ye, Jianghan Yin, Chong Ying, Jiale Yu, Chen Zha, Cha Zhang, Haibin Zhang, Kaili Zhang, Yiming Zhang, Han Zhao, Youwei Zhao, Liang Zhou, Qingling Zhu, Chao-Yang Lu, Cheng-Zhi Peng, Xiaobo Zhu, and Jian-Wei Pan. Strong quantum computational advantage using a superconducting quantum processor. Phys. Rev. Lett., 127: 180501, Oct 2021. 10.1103/PhysRevLett.127.180501. URL https://link.aps.org/doi/10.1103/PhysRevLett.127.180501.
https://doi.org/10.1103/PhysRevLett.127.180501
[7] Lars S Madsen, Fabian Laudenbach, Mohsen Falamarzi Askarani, Fabien Rortais, Trevor Vincent, Jacob FF Bulmer, Filippo M Miatto, Leonhard Neuhaus, Lukas G Helt, Matthew J Collins, et al. Quantum computational advantage with a programmable photonic processor. Nature, 606 (7912): 75–81, 2022. https://doi.org/10.1038/s41586-022-04725-x.
https://doi.org/10.1038/s41586-022-04725-x
[8] Sergio Boixo, Sergei V Isakov, Vadim N Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J Bremner, John M Martinis, and Hartmut Neven. Characterizing quantum supremacy in near-term devices. Nature Physics, 14 (6): 595–600, 2018. https://doi.org/10.1038/s41567-018-0124-x.
https://doi.org/10.1038/s41567-018-0124-x
[9] Adam Bouland, Bill Fefferman, Chinmay Nirkhe, and Umesh Vazirani. On the complexity and verification of quantum random circuit sampling. Nature Physics, 15 (2): 159–163, 2019. https://doi.org/10.1038/s41567-018-0318-2.
https://doi.org/10.1038/s41567-018-0318-2
[10] Craig S. Hamilton, Regina Kruse, Linda Sansoni, Sonja Barkhofen, Christine Silberhorn, and Igor Jex. Gaussian boson sampling. Phys. Rev. Lett., 119: 170501, Oct 2017. 10.1103/PhysRevLett.119.170501. URL https://link.aps.org/doi/10.1103/PhysRevLett.119.170501.
https://doi.org/10.1103/PhysRevLett.119.170501
[11] Regina Kruse, Craig S Hamilton, Linda Sansoni, Sonja Barkhofen, Christine Silberhorn, and Igor Jex. Detailed study of gaussian boson sampling. Phys. Rev. A, 100 (3): 032326, 2019. 10.1103/PhysRevA.100.032326. URL https://link.aps.org/doi/10.1103/PhysRevA.100.032326.
https://doi.org/10.1103/PhysRevA.100.032326
[12] Abhinav Deshpande, Arthur Mehta, Trevor Vincent, Nicolás Quesada, Marcel Hinsche, Marios Ioannou, Lars Madsen, Jonathan Lavoie, Haoyu Qi, Jens Eisert, et al. Quantum computational advantage via high-dimensional gaussian boson sampling. Science advances, 8 (1): eabi7894, 2022. https://doi.org/10.1126/sciadv.abi7894.
https://doi.org/10.1126/sciadv.abi7894
[13] Daniel Grier, Daniel J. Brod, Juan Miguel Arrazola, Marcos Benicio de Andrade Alonso, and Nicolás Quesada. The Complexity of Bipartite Gaussian Boson Sampling. Quantum, 6: 863, November 2022. ISSN 2521-327X. 10.22331/q-2022-11-28-863. URL https://doi.org/10.22331/q-2022-11-28-863.
https://doi.org/10.22331/q-2022-11-28-863
[14] Nicolás Quesada, Juan Miguel Arrazola, and Nathan Killoran. Gaussian boson sampling using threshold detectors. Phys. Rev. A, 98: 062322, 12 2018. 10.1103/PhysRevA.98.062322. URL https://link.aps.org/doi/10.1103/PhysRevA.98.062322.
https://doi.org/10.1103/PhysRevA.98.062322
[15] Jacob FF Bulmer, Bryn A Bell, Rachel S Chadwick, Alex E Jones, Diana Moise, Alessandro Rigazzi, Jan Thorbecke, Utz-Uwe Haus, Thomas Van Vaerenbergh, Raj B Patel, et al. The boundary for quantum advantage in gaussian boson sampling. Science advances, 8 (4): eabl9236, 2022a. https://doi.org/10.1126/sciadv.abl9236.
https://doi.org/10.1126/sciadv.abl9236
[16] Nicolás Quesada, Rachel S. Chadwick, Bryn A. Bell, Juan Miguel Arrazola, Trevor Vincent, Haoyu Qi, and Raúl García-Patrón. Quadratic speed-up for simulating gaussian boson sampling. PRX Quantum, 3: 010306, Jan 2022. 10.1103/PRXQuantum.3.010306. URL https://link.aps.org/doi/10.1103/PRXQuantum.3.010306.
https://doi.org/10.1103/PRXQuantum.3.010306
[17] Nicolás Quesada and Juan Miguel Arrazola. Exact simulation of gaussian boson sampling in polynomial space and exponential time. Phys. Rev. Res., 2: 023005, Apr 2020. 10.1103/PhysRevResearch.2.023005. URL https://link.aps.org/doi/10.1103/PhysRevResearch.2.023005.
https://doi.org/10.1103/PhysRevResearch.2.023005
[18] Brajesh Gupt, Juan Miguel Arrazola, Nicolás Quesada, and Thomas R Bromley. Classical benchmarking of gaussian boson sampling on the titan supercomputer. Quantum Information Processing, 19 (8): 1–14, 2020. https://doi.org/10.1007/s11128-020-02713-6.
https://doi.org/10.1007/s11128-020-02713-6
[19] J. Eli Bourassa, Nicolás Quesada, Ilan Tzitrin, Antal Száva, Theodor Isacsson, Josh Izaac, Krishna Kumar Sabapathy, Guillaume Dauphinais, and Ish Dhand. Fast simulation of bosonic qubits via gaussian functions in phase space. PRX Quantum, 2: 040315, Oct 2021. 10.1103/PRXQuantum.2.040315. URL https://link.aps.org/doi/10.1103/PRXQuantum.2.040315.
https://doi.org/10.1103/PRXQuantum.2.040315
[20] Ulysse Chabaud and Mattia Walschaers. Resources for bosonic quantum computational advantage. Phys. Rev. Lett., 130: 090602, Mar 2023. 10.1103/PhysRevLett.130.090602. URL https://link.aps.org/doi/10.1103/PhysRevLett.130.090602.
https://doi.org/10.1103/PhysRevLett.130.090602
[21] Benjamin Villalonga, Murphy Yuezhen Niu, Li Li, Hartmut Neven, John C Platt, Vadim N Smelyanskiy, and Sergio Boixo. Efficient approximation of experimental gaussian boson sampling. arXiv preprint arXiv:2109.11525, 2021. https://doi.org/10.48550/arXiv.2109.11525.
https://doi.org/10.48550/arXiv.2109.11525
arXiv:2109.11525
[22] Haoyu Qi, Daniel J. Brod, Nicolás Quesada, and Raúl García-Patrón. Regimes of classical simulability for noisy gaussian boson sampling. Phys. Rev. Lett., 124: 100502, 3 2020. 10.1103/PhysRevLett.124.100502. URL https://link.aps.org/doi/10.1103/PhysRevLett.124.100502.
https://doi.org/10.1103/PhysRevLett.124.100502
[23] Soran Jahangiri, Juan Miguel Arrazola, Nicolás Quesada, and Nathan Killoran. Point processes with gaussian boson sampling. Phys. Rev. E, 101: 022134, Feb 2020. 10.1103/PhysRevE.101.022134. URL https://link.aps.org/doi/10.1103/PhysRevE.101.022134.
https://doi.org/10.1103/PhysRevE.101.022134
[24] M. D. Reid and D. F. Walls. Violations of classical inequalities in quantum optics. Phys. Rev. A, 34: 1260–1276, Aug 1986. 10.1103/PhysRevA.34.1260. URL https://link.aps.org/doi/10.1103/PhysRevA.34.1260.
https://doi.org/10.1103/PhysRevA.34.1260
[25] Peter D Drummond and Mark Hillery. The quantum theory of nonlinear optics. Cambridge University Press, 2014.
[26] Saleh Rahimi-Keshari, Timothy C. Ralph, and Carlton M. Caves. Sufficient conditions for efficient classical simulation of quantum optics. Phys. Rev. X, 6: 021039, Jun 2016. 10.1103/PhysRevX.6.021039. URL https://link.aps.org/doi/10.1103/PhysRevX.6.021039.
https://doi.org/10.1103/PhysRevX.6.021039
[27] Saleh Rahimi-Keshari, Austin P. Lund, and Timothy C. Ralph. What can quantum optics say about computational complexity theory? Phys. Rev. Lett., 114: 060501, 2 2015. 10.1103/PhysRevLett.114.060501. URL https://link.aps.org/doi/10.1103/PhysRevLett.114.060501.
https://doi.org/10.1103/PhysRevLett.114.060501
[28] Brajesh Gupt, Josh Izaac, and Nicolás Quesada. The walrus: a library for the calculation of hafnians, hermite polynomials and gaussian boson sampling. Journal of Open Source Software, 4 (44): 1705, 2019. 10.21105/joss.01705. URL https://doi.org/10.21105/joss.01705.
https://doi.org/10.21105/joss.01705
[29] Peter D. Drummond, Bogdan Opanchuk, A. Dellios, and M. D. Reid. Simulating complex networks in phase space: Gaussian boson sampling. Phys. Rev. A, 105: 012427, 1 2022. 10.1103/PhysRevA.105.012427. URL https://link.aps.org/doi/10.1103/PhysRevA.105.012427.
https://doi.org/10.1103/PhysRevA.105.012427
[30] Martin Houde and Nicolás Quesada. Waveguided sources of consistent, single-temporal-mode squeezed light: The good, the bad, and the ugly. AVS Quantum Science, 5 (1), 02 2023. ISSN 2639-0213. https://doi.org/10.1116/5.0133009. 011404.
https://doi.org/10.1116/5.0133009
[31] Alessio Serafini. Quantum continuous variables: a primer of theoretical methods. CRC press, 2017.
[32] Stephen Barnett and Paul M Radmore. Methods in theoretical quantum optics, volume 15. Oxford University Press, 2002.
[33] Han-Sen Zhong, Hui Wang, Yu-Hao Deng, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Jian Qin, Dian Wu, Xing Ding, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Chao-Yang Lu, and Jian-Wei Pan. Experimental raw data of “quantum computational advantage using photons”. https://quantum.ustc.edu.cn/web/en/node/915, 12 2020b.
https://quantum.ustc.edu.cn/web/en/node/915
[34] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, Si-Qiu Gong, Hao Su, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Jelmer J. Renema, Chao-Yang Lu, and Jian-Wei Pan. Raw data of jiuzhang 2.0 for sharing. https://quantum.ustc.edu.cn/web/en/node/951, 4 2021b.
https://quantum.ustc.edu.cn/web/en/node/951
[35] G.S. Thekkadath, S. Sempere-Llagostera, B.A. Bell, R.B. Patel, M.S. Kim, and I.A. Walmsley. Experimental demonstration of gaussian boson sampling with displacement. PRX Quantum, 3: 020336, May 2022. 10.1103/PRXQuantum.3.020336. URL https://link.aps.org/doi/10.1103/PRXQuantum.3.020336.
https://doi.org/10.1103/PRXQuantum.3.020336
[36] J. F. F. Bulmer, S. Paesani, R. S. Chadwick, and N. Quesada. Threshold detection statistics of bosonic states. Phys. Rev. A, 106: 043712, Oct 2022b. 10.1103/PhysRevA.106.043712. URL https://link.aps.org/doi/10.1103/PhysRevA.106.043712.
https://doi.org/10.1103/PhysRevA.106.043712
[37] D. S. Phillips, M. Walschaers, J. J. Renema, I. A. Walmsley, N. Treps, and J. Sperling. Benchmarking of gaussian boson sampling using two-point correlators. Phys. Rev. A, 99: 023836, Feb 2019. 10.1103/PhysRevA.99.023836. URL https://link.aps.org/doi/10.1103/PhysRevA.99.023836.
https://doi.org/10.1103/PhysRevA.99.023836
[38] R. A. Fisher and J. Wishart. The Derivation of the Pattern Formulae of Two-Way Partitions from those of Simpler Patterns. Proceedings of the London Mathematical Society, s2-33 (1): 195–208, 1932. https://doi.org/10.1112/plms/s2-33.1.195.
https://doi.org/10.1112/plms/s2-33.1.195
[39] Yanic Cardin and Nicolás Quesada. Photon-number moments and cumulants of gaussian states. arXiv preprint arXiv:2212.06067, 2022. https://doi.org/10.48550/arXiv.2212.06067.
https://doi.org/10.48550/arXiv.2212.06067
arXiv:2212.06067
[40] H. D. Ursell. The evaluation of gibbs’ phase-integral for imperfect gases. Mathematical Proceedings of the Cambridge Philosophical Society, 23 (6): 685–697, 1927. 10.1017/S0305004100011191.
https://doi.org/10.1017/S0305004100011191
[41] M Duneau, Daniel Iagolnitzer, and B Souillard. Decrease properties of truncated correlation functions and analyticity properties for classical lattices and continuous systems. Communications in Mathematical Physics, 31 (3): 191–208, 1973. https://doi.org/10.1007/BF01646265.
https://doi.org/10.1007/BF01646265
[42] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. URL http://www.R-project.org/. ISBN 3-900051-07-0.
http://www.R-project.org/
[43] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17: 261–272, 2020. 10.1038/s41592-019-0686-2.
https://doi.org/10.1038/s41592-019-0686-2
[44] Ágoston Kaposi, Zoltán Kolarovszki, Tamás Kozsik, Zoltán Zimborás, and Péter Rakyta. Polynomial speedup in torontonian calculation by a scalable recursive algorithm. arXiv preprint arXiv:2109.04528, 2021. https://doi.org/10.48550/arXiv.2109.04528.
https://doi.org/10.48550/arXiv.2109.04528
arXiv:2109.04528
[45] Marco Bentivegna, Nicolò Spagnolo, Chiara Vitelli, Daniel J. Brod, Andrea Crespi, Fulvio Flamini, Roberta Ramponi, Paolo Mataloni, Roberto Osellame, Ernesto F. Galvão, and Fabio Sciarrino. Bayesian approach to boson sampling validation. International Journal of Quantum Information, 12 (07n08): 1560028, 2014. https://doi.org/10.1142/S021974991560028X.
https://doi.org/10.1142/S021974991560028X
[46] Javier Martínez-Cifuentes and Nicolás Quesada. torontonian-julia. https://github.com/polyquantique/torontonian-julia, 09 2022.
https://github.com/polyquantique/torontonian-julia
[47] Jeffrey Sarnoff and JuliaMath. DoubleFloats, 6 2022. URL https://github.com/JuliaMath/DoubleFloats.jl.
https://github.com/JuliaMath/DoubleFloats.jl
[48] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59 (1): 65–98, 2017. https://doi.org/10.1137/141000671.
https://doi.org/10.1137/141000671
[49] Yuan Yao, Filippo Miatto, and Nicolás Quesada. The recursive representation of gaussian quantum mechanics. arXiv preprint arXiv:2209.06069, 2022. https://doi.org/10.48550/arXiv.2209.06069.
https://doi.org/10.48550/arXiv.2209.06069
arXiv:2209.06069
[50] N Quesada, LG Helt, J Izaac, JM Arrazola, R Shahrokhshahi, CR Myers, and KK Sabapathy. Simulating realistic non-gaussian state preparation. Physical Review A, 100 (2): 022341, 2019. https://doi.org/10.1103/PhysRevA.100.022341.
https://doi.org/10.1103/PhysRevA.100.022341
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