|
|
Variational quantum simulation of thermal statistical states on a superconducting quantum processer |
Xue-Yi Guo(郭学仪)1, Shang-Shu Li(李尚书)1,2, Xiao Xiao(效骁)3, Zhong-Cheng Xiang(相忠诚)1, Zi-Yong Ge(葛自勇)1,2, He-Kang Li(李贺康)1,2, Peng-Tao Song(宋鹏涛)1,2, Yi Peng(彭益)1,2, Zhan Wang(王战)1,2, Kai Xu(许凯)1,4, Pan Zhang(张潘)5,6,7,†, Lei Wang(王磊)1,8,‡, Dong-Ning Zheng(郑东宁)1,2,4,8,§, and Heng Fan(范桁)1,2,4,9,¶ |
1 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; 2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China; 3 The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; 4 CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100190, China; 5 Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China; 6 School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; 7 International Centre for Theoretical Physics Asia-Pacific, Beijing/Hangzhou, China; 8 Songshan Lake Materials Laboratory, Dongguan 523808, China; 9 Beijing Academy of Quantum Information Sciences, Beijing 100193, China |
|
|
Abstract Quantum computers promise to solve finite-temperature properties of quantum many-body systems, which is generally challenging for classical computers due to high computational complexities. Here, we report experimental preparations of Gibbs states and excited states of Heisenberg $XX$ and $XXZ$ models by using a 5-qubit programmable superconducting processor. In the experiments, we apply a hybrid quantum-classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits. We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits, which enable us to prepare excited states at arbitrary energy density. We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error. Based on numerical results, we further show that the time complexity of our approach scales polynomially in the number of qubits, revealing its potential in solving large-scale problems.
|
Received: 27 November 2022
Revised: 01 December 2022
Accepted manuscript online: 02 December 2022
|
PACS:
|
03.67.Lx
|
(Quantum computation architectures and implementations)
|
|
03.67.-a
|
(Quantum information)
|
|
05.30.-d
|
(Quantum statistical mechanics)
|
|
Fund: We thank Zhengan Wang, Ruizhen Huang and Tao Xiang for useful discussions. Project supported by the State Key Development Program for Basic Research of China (Grant No. 2017YFA0304300), the National Natural Science Foundation of China (Grant Nos. 11934018, 11747601, and 11975294), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB28000000), Scientific Instrument Developing Project of Chinese Academy of Sciences (Grant No. YJKYYQ20200041), Beijing Natural Science Foundation (Grant No. Z200009), the Key-Area Research and Development Program of Guangdong Province, China (Grant No. 2020B0303030001), and Chinese Academy of Sciences (Grant No. QYZDB-SSW-SYS032). |
Corresponding Authors:
Pan Zhang, Lei Wang, Dong-Ning Zheng, Heng Fan
E-mail: panzhang@itp.ac.cn;wanglei@iphy.ac.cn;dzheng@iphy.ac.cn;hfan@iphy.ac.cn
|
Cite this article:
Xue-Yi Guo(郭学仪), Shang-Shu Li(李尚书), Xiao Xiao(效骁), Zhong-Cheng Xiang(相忠诚), Zi-Yong Ge(葛自勇), He-Kang Li(李贺康), Peng-Tao Song(宋鹏涛), Yi Peng(彭益), Zhan Wang(王战), Kai Xu(许凯), Pan Zhang(张潘), Lei Wang(王磊), Dong-Ning Zheng(郑东宁), and Heng Fan(范桁) Variational quantum simulation of thermal statistical states on a superconducting quantum processer 2023 Chin. Phys. B 32 010307
|
[1] Cerezo M, Arrasmith A, Babbush R, Benjamin S C, Endo S, Fujii K, McClean J R, Mitarai K, Yuan X, Cincio L and Coles P J 2021 Nat. Rev. Phys. 3 625 [2] Preskill J 2018 Quantum 2 79 [3] Moll N, Barkoutsos P, Bishop L S, Chow J M, Cross A, Egger D J, Filipp S, Fuhrer A, Gambetta J M, Ganzhorn M, Kandala A, Mezzacapo A, Müller P, Riess W, Salis G, Smolin J, Tavernelli I and Temme K 2018 Quantum Sci. Technol. 3 030503 [4] Lau J W Z, Lim K H, Shrotriya H and Kwek L C 2022 AAPPS Bull. 32 27 [5] Peruzzo A, McClean J, Shadbolt P, Yung M H, Zhou X Q, Love P J, Aspuru-Guzik A and O'Brien J L 2014 Nat. Commun. 5 4213 [6] Colless J I, Ramasesh V V, Dahlen D, Blok M S, Kimchi-Schwartz M E, McClean J R, Carter J, de Jong W A and Siddiqi I 2018 Phys. Rev. X 8 011021 [7] Google AI Quantum and Collaborators 2020 Science 369 1084 [8] Hempel C, Maier C, Romero J, McClean J, Monz T, Shen H, Jurcevic P, Lanyon B P, Love P, Babbush R, Aspuru-Guzik A, Blatt R and Roos C F 2018 Phys. Rev. X 8 031022 [9] Kandala A, Mezzacapo A, Temme K, Takita M, Brink M, Chow J M and Gambetta J M 2017 Nature 549 242 [10] O'Malley P J J, Babbush R, Kivlichan I D, et al. 2016 Phys. Rev. X 6 031007 [11] Kokail C, Maier C, van Bijnen R, Brydges T, Joshi M K, Jurcevic P, Muschik C A, Silvi P, Blatt R, Roos C F and Zoller P 2019 Nature 569 355 [12] Wei S, Li H and Long G 2020 Research 2020 [13] Farhi E, Goldstone J and Gutmann S A 2014 arXiv: 1411.4028 [14] Zhou L, Wang S T, Choi S, Pichler H and Lukin M D 2020 Phys. Rev. X 10 021067 [15] McArdle S, Jones T, Endo S, Li Y, Benjamin S C and Yuan X 2019 npj Quantum Information 5 75 [16] Endo S, Sun J, Li Y, Benjamin S C and Yuan X 2020 Phys. Rev. Lett. 125 010501 [17] Deutsch J M 1991 Phys. Rev. A 43 2046 [18] Srednicki M 1994 Phys. Rev. E 50 888 [19] Abanin D A, Altman E, Bloch I and Serbyn M 2019 Rev. Mod. Phys. 91 021001 [20] Guo Q, Cheng C, Sun Z H, et al. 2021 Nat. Phys. 17 234 [21] Serbyn M, Abanin D A and Papić Z 2021 Nat. Phys. 17 675 [22] Terhal B M and DiVincenzo D P 2000 Phys. Rev. A 61 022301 [23] Poulin D and Wocjan P 2009 Phys. Rev. Lett. 103 220502 [24] Temme K, Osborne T J, Vollbrecht K G, Poulin D and Verstraete F 2011 Nature 471 87 [25] Brandão F G S L and Kastoryano M J 2019 Commun. Math. Phys. 365 1 [26] Wu J and Hsieh T H 2019 Phys. Rev. Lett. 123 220502 [27] Zhu D, Johri S, Linke N M, Landsman K A, Alderete C H, Nguyen N H, Matsuura A Y, Hsieh T H and Monroe C 2020 Proc. Natl. Acad. Sci. USA 117 25402 [28] Sagastizabal R, Premaratne S P, Klaver B A, Rol M A, Negîrneac V, Moreira M S, Zou X, Johri S, Muthusubramanian N, Beekman M, Zachariadis C, Ostroukh V P, Haider N, Bruno A, Matsuura A Y and DiCarlo L 2021 npj Quantum Inf 7 1 [29] Wang Y, Li G and Wang X 2021 Phys. Rev. Applied 16 054035 [30] Motta M, Sun C, Tan A T K, O'Rourke M J, Ye E, Minnich A J, Brandão F G S L and Chan G K L 2020 Nat. Phys. 16 205 [31] Sun S N, Motta M, Tazhigulov R N, Tan A T K, Chan G K L and Minnich A J 2021 PRX Quantum 2 010317 [32] Verdon G, Marks J, Nanda S, Leichenauer S and Hidary J 2019 arXiv: 1910.02071 [33] Liu J G, Mao L, Zhang P and Wang L 2021 Mach. Learn.: Sci. Technol. 2 025011 [34] Santagati R, Wang J, Gentile A A, Paesani S, Wiebe N, McClean J R, Morley-Short S, Shadbolt P J, Bonneau D, Silverstone J W, Tew D P, Zhou X, O'Brien J L and Thompson M G 2018 Sci. Adv. 4 eaap9646 [35] Higgott O, Wang D and Brierley S 2019 Quantum 3 156 [36] Jones T, Endo S, McArdle S, Yuan X and Benjamin S C 2019 Phys. Rev. A 99 062304 [37] Nakanishi K M, Mitarai K and Fujii K 2019 Phys. Rev. Research 1 033062 [38] Wen J, Lv D, Yung M H and Long G L 2021 Quantum Eng. 3 e80 [39] Martyn J and Swingle B 2019 Phys. Rev. A 100 032107 [40] Huber A 1968 Mathematical Methods in Solid State and Superfluid Theory pp. 364-92 [41] Zhu D, Linke N M, Benedetti M, Landsman K A, Nguyen N H, Alderete C H, Perdomo-Ortiz A, Korda N, Garfoot A, Brecque C, Egan L, Perdomo O and Monroe C 2019 Sci. Adv. 5 eaaw9918 [42] Williams R J 1992 Mach Learn 8 229 [43] Goodfellow I, Bengio Y and Courville A 2016 Deep Learning [44] Mnih A and Gregor K 2014 arXiv: 1402.0030 [45] Wu D, Wang L and Zhang P 2019 Phys. Rev. Lett. 122 080602 [46] Guo X Y, Ge Z Y, Li H, Wang Z, Zhang Y R, Song P, Xiang Z, Song X, Jin Y, Lu L, Xu K, Zheng D and Fan H 2021 npj Quantum Information 7 51 [47] Lucero E, Kelly J, Bialczak R C, Lenander M, Mariantoni M, Neeley M, O'Connell A D, Sank D, Wang H, Weides M, Wenner J, Yamamoto T, Cleland A N and Martinis J M 2010 Phys. Rev. A 82 042339 [48] Barends R, Kelly J, Megrant A, Sank D, Jeffrey E, Chen Y, Yin Y, Chiaro B, Mutus J, Neill C, O'Malley P, Roushan P, Wenner J, White T C, Cleland A N and Martinis J M 2013 Phys. Rev. Lett. 111 080502 [49] Herrmann J, Lacroix N, Andersen C K, Remm A, Lazar S, Besse J C, Walter T, Wallraff A, Eichler C and Collodo M C 2020 Phys. Rev. Lett. 125 240502 [50] Bishop C M 2006 Pattern Recognition and Machine Learning (New York: Springer Science) [51] Schuld M, Bergholm V, Gogolin C, Izaac J and Killoran N 2019 Phys. Rev. A 99 032331 [52] Spall J C 2000 IEEE Trans. Automat. Contr. 45 1839 [53] Kingma D P and Ba J 2017 arXiv: 1412.6980 [54] Harrow A W and Napp J C 2021 Phys. Rev. Lett. 126 140502 |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|