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Surveying structural complexity in quantum many-body systems. (English) Zbl 1487.81119

Summary: Quantum many-body systems exhibit a rich and diverse range of exotic behaviours, owing to their underlying non-classical structure. These systems present a deep structure beyond those that can be captured by measures of correlation and entanglement alone. Using tools from complexity science, we characterise such structure. We investigate the structural complexities that can be found within the patterns that manifest from the observational data of these systems. In particular, using two prototypical quantum many-body systems as test cases – the one-dimensional quantum Ising and Bose-Hubbard models – we explore how different information-theoretic measures of complexity are able to identify different features of such patterns. This work furthers the understanding of fully-quantum notions of structure and complexity in quantum systems and dynamics.

MSC:

81S25 Quantum stochastic calculus
82B20 Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics
68Q12 Quantum algorithms and complexity in the theory of computing
81V73 Bosonic systems in quantum theory
81P40 Quantum coherence, entanglement, quantum correlations
90C60 Abstract computational complexity for mathematical programming problems
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[1] Feynman, RP, Simulating physics with computers, Int. J. Theor. Phys., 21, 467 (1982)
[2] Bloch, I.; Dalibard, J.; Nascimbene, S., Quantum simulations with ultracold quantum gases, Nat. Phys., 8, 267 (2012)
[3] Lewenstein, M.; Sanpera, A.; Ahufinger, V., Ultracold Atoms in Optical Lattices: Simulating Quantum Many-Body Systems (2012), Oxford: Oxford University Press, Oxford · Zbl 1251.82001
[4] Johnson, TH; Clark, SR; Jaksch, D., What is a quantum simulator?, EPJ Quant. Technol., 1, 1 (2014)
[5] Jaksch, D.; Bruder, C.; Cirac, JI; Gardiner, CW; Zoller, P., Cold bosonic atoms in optical lattices, Phys. Rev. Lett., 81, 3108 (1998)
[6] Greiner, M.; Mandel, O.; Esslinger, T.; Hänsch, TW; Bloch, I., Quantum phase transition from a superfluid to a Mott insulator in a gas of ultracold atoms, Nature, 415, 39 (2002)
[7] Jaksch, D.; Zoller, P., Creation of effective magnetic fields in optical lattices: The hofstadter butterfly for cold neutral atoms, New J. Phys., 5, 56 (2003)
[8] Bloch, I.; Dalibard, J.; Zwerger, W., Many-body physics with ultracold gases, Rev. Mod. Phys., 80, 885 (2008)
[9] Baumann, K.; Guerlin, C.; Brennecke, F.; Esslinger, T., Dicke quantum phase transition with a superfluid gas in an optical cavity, Nature, 464, 1301 (2010)
[10] Ritsch, H.; Domokos, P.; Brennecke, F.; Esslinger, T., Cold atoms in cavity-generated dynamical optical potentials, Rev. Mod. Phys., 85, 553 (2013)
[11] Aidelsburger, M.; Atala, M.; Lohse, M.; Barreiro, JT; Paredes, B.; Bloch, I., Realization of the hofstadter hamiltonian with ultracold atoms in optical lattices, Phys. Rev. Lett., 111, 185301 (2013)
[12] Miyake, H.; Siviloglou, GA; Kennedy, CJ; Burton, WC; Ketterle, W., Realizing the Harper Hamiltonian with laser-assisted tunneling in optical lattices, Phys. Rev. Lett., 111, 185302 (2013)
[13] Amico, L.; Fazio, R.; Osterloh, A.; Vedral, V., Entanglement in many-body systems, Rev. Mod. Phys., 80, 517 (2008) · Zbl 1205.81009
[14] Horodecki, R.; Horodecki, P.; Horodecki, M.; Horodecki, K., Quantum entanglement, Rev. Mod. Phys., 81, 865 (2009) · Zbl 1205.81012
[15] Vidal, G., Efficient classical simulation of slightly entangled quantum computations, Phys. Rev. Lett., 91, 147902 (2003)
[16] Verstraete, F.; Cirac, JI, Matrix product states represent ground states faithfully, Phys. Rev. B, 73, 094423 (2006)
[17] Schollwöck, U., The density-matrix renormalization group in the age of matrix product states, Ann. Phys., 326, 96 (2011) · Zbl 1213.81178
[18] Orús, R., A practical introduction to tensor networks: matrix product states and projected entangled pair states, Ann. Phys., 349, 117 (2014) · Zbl 1343.81003
[19] Valdez, MA; Jaschke, D.; Vargas, DL; Carr, LD, Quantifying complexity in quantum phase transitions via mutual information complex networks, Phys. Rev. Lett., 119, 225301 (2017)
[20] Crutchfield, JP; Young, K., Inferring statistical complexity, Phys. Rev. Lett., 63, 105 (1989)
[21] Shalizi, CR; Crutchfield, JP, Computational mechanics: Pattern and prediction, structure and simplicity, J. Stat. Phys., 104, 817 (2001) · Zbl 1100.82500
[22] Crutchfield, JP, Between order and chaos, Nat. Phys., 8, 17 (2012)
[23] Gu, M.; Wiesner, K.; Rieper, E.; Vedral, V., Quantum mechanics can reduce the complexity of classical models, Nat. Commun., 3, 762 (2012)
[24] Mahoney, JR; Aghamohammadi, C.; Crutchfield, JP, Occam’s quantum strop: Synchronizing and compressing classical cryptic processes via a quantum channel, Sci. Rep., 6, 20495 (2016)
[25] Palsson, MS; Gu, M.; Ho, J.; Wiseman, HM; Pryde, GJ, Experimentally modeling stochastic processes with less memory by the use of a quantum processor, Sci. Adv., 3, e1601302 (2017)
[26] Aghamohammadi, C.; Loomis, SP; Mahoney, JR; Crutchfield, JP, Extreme quantum memory advantage for rare-event sampling, Phys. Rev. X, 8, 011025 (2018)
[27] Binder, FC; Thompson, J.; Gu, M., Practical unitary simulator for non-Markovian complex processes, Phys. Rev. Lett., 120, 240502 (2018)
[28] Elliott, TJ; Gu, M., Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes, NPJ Quant. Inf., 4, 18 (2018)
[29] Elliott, TJ; Garner, AJP; Gu, M., Memory-efficient tracking of complex temporal and symbolic dynamics with quantum simulators, New J. Phys., 21, 013021 (2019)
[30] Liu, Q.; Elliott, TJ; Binder, FC; Di Franco, C.; Gu, M., Optimal stochastic modeling with unitary quantum dynamics, Phys. Rev. A, 99, 062110 (2019)
[31] Suen, WY; Thompson, J.; Garner, AJP; Vedral, V.; Gu, M., The classical-quantum divergence of complexity in modelling spin chains, Quantum, 1, 25 (2017)
[32] Aghamohammadi, C.; Mahoney, JR; Crutchfield, JP, The ambiguity of simplicity in quantum and classical simulation, Phys. Lett. A, 381, 1223 (2017) · Zbl 1371.81017
[33] Jouneghani, F.G., Gu, M., Ho, J., Thompson, J., Suen, W.Y., Wiseman, H.M., Pryde, G.J.: Observing the ambiguity of simplicity via quantum simulations of an ising spin chain. arXiv:1711.03661 (2017)
[34] Loomis, SP; Crutchfield, JP, Strong and weak optimizations in classical and quantum models of stochastic processes, J. Stat. Phys., 176, 1317 (2019) · Zbl 1423.60170
[35] Garner, AJ; Liu, Q.; Thompson, J.; Vedral, V., Provably unbounded memory advantage in stochastic simulation using quantum mechanics, New J. Phys., 19, 103009 (2017)
[36] Aghamohammadi, C.; Mahoney, JR; Crutchfield, JP, Extreme quantum advantage when simulating classical systems with long-range interaction, Sci. Rep., 7, 6735 (2017)
[37] Thompson, J.; Garner, AJP; Mahoney, JR; Crutchfield, JP; Vedral, V.; Gu, M., Causal asymmetry in a quantum world, Phys. Rev. X, 8, 031013 (2018)
[38] Elliott, TJ; Yang, C.; Binder, FC; Garner, AJP; Thompson, J.; Gu, M., Extreme dimensionality reduction with quantum modeling, Phys. Rev. Lett., 125, 260501 (2020)
[39] Elliott, TJ; Gu, M.; Garner, AJP; Thompson, J., Quantum adaptive agents with efficient long-term memories, Phys. Rev. X, 12, 011007 (2022)
[40] Khintchine, A., Korrelationstheorie der stationären stochastischen Prozesse, Math. Ann., 109, 604 (1934) · Zbl 0008.36806
[41] Crutchfield, JP; Feldman, DP, Statistical complexity of simple 1d spin systems, Phys. Rev. E, 55, 1239R (1997)
[42] Palmer, AJ; Fairall, CW; Brewer, WA, Complexity in the atmosphere, IEEE Trans. Geosci. Remote Sens., 38, 2056 (2000)
[43] Varn, DP; Canright, GS; Crutchfield, JP, Discovering planar disorder in close-packed structures from X-ray diffraction: beyond the fault model, Phys. Rev. B, 66, 174110 (2002)
[44] Clarke, RW; Freeman, MP; Watkins, NW, Application of computational mechanics to the analysis of natural data: an example in geomagnetism, Phys. Rev. E, 67, 016203 (2003)
[45] Park, JB; Lee, JW; Yang, J-S; Jo, H-H; Moon, H-T, Complexity analysis of the stock market, Physica A, 379, 179 (2007)
[46] Li, C-B; Yang, H.; Komatsuzaki, T., Multiscale complex network of protein conformational fluctuations in single-molecule time series, Proc. Natl. Acad. Sci., 105, 536 (2008)
[47] Haslinger, R.; Klinkner, KL; Shalizi, CR, The computational structure of spike trains, Neural Comput., 22, 121 (2010) · Zbl 1187.92020
[48] Kelly, D.; Dillingham, M.; Hudson, A.; Wiesner, K., A new method for inferring hidden Markov models from noisy time sequences, PLoS ONE, 7, e29703 (2012)
[49] Mu noz, RN; Leung, A.; Zecevik, A.; Pollock, FA; Cohen, D.; van Swinderen, B.; Tsuchiya, N.; Modi, K., General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in drosophila, Phys. Rev. Res., 2, 023219 (2020)
[50] Shaw, R., The dripping faucet as a model chaotic system (1984), London: Aerial Press, London · Zbl 0842.58059
[51] Bialek, W.; Nemenman, I.; Tishby, N., Predictability, complexity, and learning, Neural Comput., 13, 2409 (2001) · Zbl 0993.68045
[52] Nielsen, MA; Chuang, IL, Quantum computation and quantum information (2010), Cambridge: Cambridge University Press, Cambridge · Zbl 1288.81001
[53] Tan, R.; Terno, DR; Thompson, J.; Vedral, V.; Gu, M., Towards quantifying complexity with quantum mechanics, Eur. Phys. J. Plus, 129, 1 (2014)
[54] Ho, M.; Gu, M.; Elliott, TJ, Robust inference of memory structure for efficient quantum modeling of stochastic processes, Phys. Rev. A, 101, 032327 (2020)
[55] Ho, M., Pradana, A., Elliott, T.J., Chew, L.Y., Gu, M.: Quantum-inspired identification of complex cellular automata. arXiv:2103.14053 (2021)
[56] Osterloh, A.; Amico, L.; Falci, G.; Fazio, R., Scaling of entanglement close to a quantum phase transition, Nature, 416, 608 (2002)
[57] Osborne, TJ; Nielsen, MA, Entanglement in a simple quantum phase transition, Phys. Rev. A, 66, 032110 (2002)
[58] Vidal, G.; Latorre, JI; Rico, E.; Kitaev, A., Entanglement in quantum critical phenomena, Phys. Rev. Lett., 90, 227902 (2003)
[59] Daley, AJ; Pichler, H.; Schachenmayer, J.; Zoller, P., Measuring entanglement growth in quench dynamics of bosons in an optical lattice, Phys. Rev. Lett., 109, 020505 (2012)
[60] Abanin, DA; Demler, E., Measuring entanglement entropy of a generic many-body system with a quantum switch, Phys. Rev. Lett., 109, 020504 (2012)
[61] Islam, R.; Ma, R.; Preiss, PM; Tai, ME; Lukin, A.; Rispoli, M.; Greiner, M., Measuring entanglement entropy in a quantum many-body system, Nature, 528, 77 (2015)
[62] Elliott, TJ; Kozlowski, W.; Caballero-Benitez, SF; Mekhov, IB, Multipartite entangled spatial modes of ultracold atoms generated and controlled by quantum measurement, Phys. Rev. Lett., 114, 113604 (2015)
[63] Mattis, DC, The Theory of Magnetism Made Simple: An Introduction to Physical Concepts and to Some Useful Mathematical Methods (2006), Singapore: World Scientific, Singapore
[64] Pino, M.; Prior, J.; Somoza, AM; Jaksch, D.; Clark, SR, Reentrance and entanglement in the one-dimensional Bose-Hubbard model, Phys. Rev. A, 86, 023631 (2012)
[65] Barnett, N.; Crutchfield, JP, Computational mechanics of input-output processes: structured transformations and \(\text{the}\backslash\) epsilon-transducer, J. Stat. Phys., 161, 404 (2015) · Zbl 1327.82002
[66] Thompson, J.; Garner, AJP; Vedral, V.; Gu, M., Using quantum theory to simplify input-output processes, NPJ Quant. Inf., 3, 6 (2017)
[67] Al-Assam, S.; Clark, SR; Jaksch, D., The tensor network theory library, J. Stat. Mech., 2017, 093102 (2017) · Zbl 1459.81004
[68] Pirvu, B.; Murg, V.; Cirac, JI; Verstraete, F., Matrix product operator representations, New J. Phys., 12, 025012 (2010) · Zbl 1360.81116
[69] White, SR, Density matrix formulation for quantum renormalization groups, Phys. Rev. Lett., 69, 2863 (1992)
[70] White, SR, Density-matrix algorithms for quantum renormalization groups, Phys. Rev. B, 48, 10345 (1993)
[71] White, SR; Huse, DA, Numerical renormalization-group study of low-lying eigenstates of the antiferromagnetic s= 1 Heisenberg chain, Phys. Rev. B, 48, 3844 (1993)
[72] Lanczos, C., An Iteration Method for the Solution of the Eigenvalue Problem of Linear Differential and Integral Operators (1950), Los Angeles: United States Government Press Office, Los Angeles · Zbl 0045.39702
[73] Davidson, ER, The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices, J. Comput. Phys., 17, 87 (1975) · Zbl 0293.65022
[74] De Chiara, G.; Rizzi, M.; Rossini, D.; Montangero, S., Density matrix renormalization group for dummies, J. Comput. Theor. Nanosci., 5, 1277 (2008)
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