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Sensible quantum benefit in quantum simulation


  • Ladd, T. D. et al. Quantum computer systems. Nature 464, 45–53 (2010).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Grumbling, E. & Horowitz, M. (eds) Quantum Computing: Progress and Prospects (Nationwide Academies Press, 2019).

  • Deutsch, I. H. Harnessing the facility of the second quantum revolution. PRX Quantum 1, 020101 (2020).

    Article 

    Google Scholar
     

  • Nielsen, M. & Chuang, I. Quantum Computation and Quantum Data tenth anniversary edn (Cambridge Univ. Press, 2010).

  • Feynman, R. P. Simulating physics with computer systems. Int. J. Theor. Phys. 21, 467–488 (1982).

    MathSciNet 
    Article 

    Google Scholar
     

  • Montanaro, A. Quantum algorithms: an summary. npj Quantum Inf. 2, 15023 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Aramon, M. et al. Physics-inspired optimization for quadratic unconstrained issues utilizing a digital annealer. Entrance. Phys. 7, 48 (2019).

  • Gibney, E. Good day quantum world! Google publishes landmark quantum supremacy declare. Nature 574, 461–462 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Arute, F. et al. Quantum supremacy utilizing a programmable superconducting processor. Nature 574, 505–510 (2019). This text experiences the demonstration of a quantum benefit with verification for a mathematical drawback designed to check the quantum {hardware}.

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Zhong, H.-S. et al. Quantum computational benefit utilizing photons. Science 370, 1460–1463 (2020).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Cirac, J. I. & Zoller, P. Targets and alternatives in quantum simulation. Nat. Phys. 8, 264–266 (2012).

    CAS 
    Article 

    Google Scholar
     

  • Georgescu, I. M., Ashhab, S. & Nori, F. Quantum simulation. Rev. Mod. Phys. 86, 153–185 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Reiher, M., Wiebe, N., Svore, Okay. M., Wecker, D. & Troyer, M. Elucidating response mechanisms on quantum computer systems. Proc. Natl Acad. Sci. USA 114, 7555–7560 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • Quintanilla, J. & Hooley, C. The strong-correlations puzzle. Phys. World 22, 32–37 (2009).

    Article 

    Google Scholar
     

  • Childs, A. M., Maslov, D., Nam, Y., Ross, N. J. & Su, Y. Towards the primary quantum simulation with quantum speedup. Proc. Natl Acad. Sci. USA 115, 9456–9461 (2018).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Lloyd, S. Common quantum simulators. Science 273, 1073–1078 (1996). This text discusses intimately how digital quantum simulation could possibly be carried out on quantum computer systems, and kinds the premise for the fault-tolerant quantum simulation protocols mentioned right here.

    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Roffe, J. Quantum error correction: an introductory information. Contemp. Phys. 60, 226–245 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Preskill, J. Quantum computing within the NISQ period and past. Quantum 2, 79 (2018).

    Article 

    Google Scholar
     

  • Buluta, I. & Nori, F. Quantum simulators. Science 326, 108–111 (2009).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Browaeys, A. & Lahaye, T. Many-body physics with individually managed Rydberg atoms. Nat. Phys. 16, 132–142 (2020).

    CAS 
    Article 

    Google Scholar
     

  • Gross, C. & Bloch, I. Quantum simulations with ultracold atoms in optical lattices. Science 357, 995–1001 (2017).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Greiner, M., Mandel, O., Esslinger, T., Hänsch, T. W. & Bloch, I. Quantum part transition from a superfluid to a Mott insulator in a fuel of ultracold atoms. Nature 415, 39–44 (2002). This text demonstrates the primary analogue quantum simulation of a strongly correlated quantum system, making use of chilly atoms in optical lattices.

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Houck, A. A., Türeci, H. E. & Koch, J. On-chip quantum simulation with superconducting circuits. Nat. Phys. 8, 292–299 (2012).

    CAS 
    Article 

    Google Scholar
     

  • Hartmann, M. J. Quantum simulation with interacting photons. J. Choose. 18, 104005 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Blatt, R. & Roos, C. F. Quantum simulations with trapped ions. Nat. Phys. 8, 277–284 (2012).

    CAS 
    Article 

    Google Scholar
     

  • Monroe, C. et al. Programmable quantum simulations of spin methods with trapped ions. Rev. Mod. Phys. 93, 025001 (2021).

    MathSciNet 
    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Aspuru-Guzik, A. & Walther, P. Photonic quantum simulators. Nat. Phys. 8, 285–291 (2012).

    CAS 
    Article 

    Google Scholar
     

  • White, A. G. Photonic quantum simulation. In 2014 OptoElectronics and Communication Convention and Australian Convention on Optical Fibre Know-how 660–661 (Optica Publishing Group, 2014).

  • Choi, J.-y et al. Exploring the many-body localization transition in two dimensions. Science 352, 1547–1552 (2016). This paper gives an necessary current demonstration of using analogue quantum simulators with chilly atoms in optical lattices to discover the dynamics of interacting particles in a disordered system, which is intractable to classical computation.

    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Chiu, C. S. et al. String patterns within the doped Hubbard mannequin. Science 365, 251–256 (2019).

    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Koepsell, J. et al. Imaging magnetic polarons within the doped Fermi–Hubbard mannequin. Nature 572, 358–362 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Semeghini, G. et al. Probing topological spin liquids on a programmable quantum simulator. Science 374, 1242–1247 (2021).

  • Satzinger, Okay. J. et al. Realizing topologically ordered states on a quantum processor. Science374, 1237–1241 (2021).

  • Bluvstein, D. et al. Controlling quantum many-body dynamics in pushed Rydberg atom arrays. Science 371, 1355–1359 (2021). This text demonstrates the state-of-the-art for observing many-body dynamics in an analogue quantum simulator with impartial atom arrays and Rydberg excitations.

    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Scholl, P. et al. Quantum simulation of 2D antiferromagnets with lots of of Rydberg atoms. Nature 595, 233–238 (2021). This text demonstrates analogue quantum simulation of dynamics with 196 spins utilizing impartial atoms in tweezer arrays.

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Zhang, J. et al. Remark of a many-body dynamical part transition with a 53-qubit quantum simulator. Nature 551, 601–604 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • Zhang, J. et al. Remark of a discrete time crystal. Nature 543, 217–220 (2017).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Barreiro, J. T. et al. An open-system quantum simulator with trapped ions. Nature 470, 486–491 (2011).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Altman, E. et al. Quantum simulators: architectures and alternatives. PRX Quantum 2, 017003 (2021).

    Article 

    Google Scholar
     

  • LeBlanc, J. P. F. et al. Options of the two-dimensional Hubbard mannequin: benchmarks and outcomes from a variety of numerical algorithms. Phys. Rev. X 5, 041041 (2015).


    Google Scholar
     

  • Zheng, B.-X. et al. Stripe order within the underdoped area of the two-dimensional Hubbard mannequin. Science 358, 1155–1160 (2017).

    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Bauer, B. et al. The ALPS mission launch 2.0: open supply software program for strongly correlated methods. J. Stat. Mech. 2011, P05001 (2011).


    Google Scholar
     

  • Becca, F. & Sorella, S. Quantum Monte Carlo Approaches for Correlated Methods (Cambridge Univ. Press, 2017).

  • Werner, P., Oka, T. & Millis, A. J. Diagrammatic Monte Carlo simulation of nonequilibrium methods. Phys. Rev. B 79, 035320 (2009).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Troyer, M. & Wiese, U.-J. Computational complexity and basic limitations to fermionic quantum Monte Carlo simulations. Phys. Rev. Lett. 94, 170201 (2005).

    PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Eisert, J. Entangling energy and quantum circuit complexity. Phys. Rev. Lett.127, 020501 (2021).

  • Swingle, B., Bentsen, G., Schleier-Smith, M. & Hayden, P. Measuring the scrambling of quantum info. Phys. Rev. A 94, 040302 (2016).

    MathSciNet 
    Article 
    ADS 

    Google Scholar
     

  • Hatano, N. & Suzuki, M. in Quantum Annealing and Different Optimization Strategies (eds Das, A. & Chakrabarti, B. Okay.) 37–68 (Lecture Notes in Physics, Springer, 2005).

  • Childs, A. M., Su, Y., Tran, M. C., Wiebe, N. & Zhu, S. Concept of Trotter error with commutator scaling. Phys. Rev. X 11, 011020 (2021).

    CAS 

    Google Scholar
     

  • Heyl, M., Hauke, P. & Zoller, P. Quantum localization bounds trotter errors in digital quantum simulation. Sci. Adv. 5, eaau8342 (2019).

    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • Wecker, D., Bauer, B., Clark, B. Okay., Hastings, M. B. & Troyer, M. Gate-count estimates for performing quantum chemistry on small quantum computer systems. Phys. Rev. A 90, 022305 (2014).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Wecker, D. et al. Fixing strongly correlated electron fashions on a quantum pc. Phys. Rev. A 92, 062318 (2015).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Kliesch, M., Gogolin, C. & Eisert, J. Lieb–Robinson Bounds and the Simulation of Time-Evolution of Native Observables in Lattice Methods 301–318 (Springer, 2014).

  • Schollwöck, U. The density-matrix renormalization group within the age of matrix product states. Ann. Phys. 326, 96–192 (2011).

    MathSciNet 
    MATH 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Verstraete, F., Murg, V. & Cirac, J. I. Matrix product states, projected entangled pair states, and variational renormalization group strategies for quantum spin methods. Adv. Phys. 57, 143–224 (2008).

    Article 
    ADS 

    Google Scholar
     

  • Vidal, G. Environment friendly simulation of one-dimensional quantum many-body methods. Phys. Rev. Lett. 93, 040502 (2004). This text launched classical simulation of one-dimensional many-body methods utilizing matrix product states, which offer the current state-of-the-art in classical simulation of quench dynamics in strongly interacting methods.

    PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Amico, L., Fazio, R., Osterloh, A. & Vedral, V. Entanglement in many-body methods. Rev. Mod. Phys. 80, 517–576 (2008).

    MathSciNet 
    CAS 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • Albash, T. & Lidar, D. A. Adiabatic quantum computation. Rev. Mod. Phys. 90, 015002 (2018).

    MathSciNet 
    Article 
    ADS 

    Google Scholar
     

  • Kempe, J., Kitaev, A. & Regev, O. The complexity of the native Hamiltonian drawback. SIAM J. Comput. 35, 1070–1097 (2006).

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • Poggi, P. M., Lysne, N. Okay., Kuper, Okay. W., Deutsch, I. H. & Jessen, P. S. Quantifying the sensitivity to errors in analog quantum simulation. PRX Quantum 1, 020308 (2020).

    Article 

    Google Scholar
     

  • Lanyon, B. P. et al. Common digital quantum simulation with trapped ions. Science 334, 57–61 (2011).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Martinez, E. A. et al. Actual-time dynamics of lattice gauge theories with a few-qubit quantum pc. Nature 534, 516–519 (2016).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Berry, D. W., Childs, A. M., Cleve, R., Kothari, R. & Somma, R. D. Simulating Hamiltonian dynamics with a truncated taylor collection. Phys. Rev. Lett. 114, 090502 (2015).

    PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Haah, J., Hastings, M. B., Kothari, R. & Low, G. H. Quantum algorithm for simulating actual time evolution of lattice Hamiltonians. SIAM J. Comput. FOCS18-250-FOCS18-284 (2021).

  • Aharonov, D. & Ta-Shma, A. Adiabatic quantum state era and statistical zero information. In Proc. Thirty-Fifth Annual ACM Symposium on Concept of Computing, STOC ’03 20–29 (Affiliation for Computing Equipment, 2003).

  • Low, G. H. & Chuang, I. L. Hamiltonian simulation by qubitization. Quantum 3, 163 (2019).

    Article 

    Google Scholar
     

  • Flannigan, S. et al. Propagation of errors and quantitative quantum simulation with quantum benefit. Preprint at https://arxiv.org/abs/2204.13644 (2022).

  • Morgado, M. & Whitlock, S. Quantum simulation and computing with rydberg-interacting qubits. AVS Quantum Sci. 3, 023501 (2021).

    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Poulin, D. et al. The Trotter step dimension required for correct quantum simulation of quantum chemistry. Quantum Inf. Comput. 15, 361–384 (2015).

    MathSciNet 
    CAS 

    Google Scholar
     

  • Sornborger, A. T. & Stewart, E. D. Increased-order strategies for simulations on quantum computer systems. Phys. Rev. A 60, 1956–1965 (1999).

    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Hastings, M. B., Wecker, D., Bauer, B. & Troyer, M. Enhancing quantum algorithms for quantum chemistry. Quantum Inf. Comput. 15, 1–21 (2015).

    MathSciNet 
    CAS 

    Google Scholar
     

  • Bocharov, A., Roetteler, M. & Svore, Okay. M. Environment friendly synthesis of common repeat-until-success quantum circuits. Phys. Rev. Lett. 114, 080502 (2015).

    PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Gidney, C. Halving the price of quantum addition. Quantum 2, 74 (2018).

    Article 

    Google Scholar
     

  • Carrasco, J., Elben, A., Kokail, C., Kraus, B. & Zoller, P. Theoretical and experimental views of quantum verification. PRX Quantum 2, 010102 (2021).

    Article 

    Google Scholar
     

  • Eisert, J. et al. Quantum certification and benchmarking. Nat. Rev. Phys. 2, 382–390 (2020).

    Article 

    Google Scholar
     

  • Elben, A. et al. Cross-platform verification of intermediate scale quantum units. Phys. Rev. Lett. 124, 010504 (2020).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Bairey, E., Arad, I. & Lindner, N. H. Studying a neighborhood Hamiltonian from native measurements. Phys. Rev. Lett. 122, 020504 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Evans, T. J., Harper, R. & Flammia, S. T. Scalable Bayesian Hamiltonian studying. Preprint at https://arxiv.org/abs/1912.07636 (2019).

  • Li, Z., Zou, L. & Hsieh, T. H. Hamiltonian tomography by way of quantum quench. Phys. Rev. Lett. 124, 160502 (2020).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Valenti, A., van Nieuwenburg, E., Huber, S. & Greplova, E. Hamiltonian studying for quantum error correction. Phys. Rev. Res. 1, 033092 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Wang, J. et al. Experimental quantum Hamiltonian studying. Nat. Phys. 13, 551–555 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Abanin, D. A., Altman, E., Bloch, I. & Serbyn, M. Colloquium: Many-body localization, thermalization, and entanglement. Rev. Mod. Phys. 91, 021001 (2019).

    MathSciNet 
    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Ozawa, T. et al. Topological photonics. Rev. Mod. Phys. 91, 015006 (2019).

    MathSciNet 
    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Turner, C. J., Michailidis, A. A., Abanin, D. A., Serbyn, M. & Papić, Z. Weak ergodicity breaking from quantum many-body scars. Nat. Phys. 14, 745–749 (2018).

    CAS 
    Article 

    Google Scholar
     

  • Bañuls, M. C. et al. Simulating lattice gauge theories inside quantum applied sciences. Eur. Phys. J. D 74, 165 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Bentsen, G. et al. Treelike interactions and quick scrambling with chilly atoms. Phys. Rev. Lett. 123, 130601 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Periwal, A. et al. Programmable interactions and emergent geometry in an atomic array. Nature 600, 630–635 (2021).

  • Argüello-Luengo, J., González-Tudela, A., Shi, T., Zoller, P. & Cirac, J. I. Analogue quantum chemistry simulation. Nature 574, 215–218 (2019).

    PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Cubitt, T., Montanaro, A. & Piddock, S. Common quantum Hamiltonians. Proc. Natl Acad. Sci. USA 115, 9497–9502 (2018).

  • Zhou, L. & Aharonov, D. Strongly common Hamiltonian simulators. Preprint at https://arxiv.org/abs/2102.02991 (2021).

  • Kaubruegger, R. et al. Variational spin-squeezing algorithms on programmable quantum sensors. Phys. Rev. Lett. 123, 260505 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Liu, H. et al. Prospects of quantum computing for molecular sciences. Mater. Concept 6, 11 (2022).

  • Bassman, L. et al. Simulating quantum supplies with digital quantum computer systems. Quant. Sci. Technol. 6, 043002 (2021).

  • Ma, H., Govoni, M. & Galli, G. Quantum simulations of supplies on near-term quantum computer systems. npj Comput. Mater. 6, 85 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Rieger, H. in Quantum Annealing and Different Optimization Strategies (eds Das, A. & Chakrabarti, B. Okay.) 299–324 (Lecture Notes in Physics, Springer, 2005).

  • Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H. & Oliver, W. D. Views of quantum annealing: strategies and implementations. Rep. Prog. Phys. 83, 054401 (2020).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Lamata, L., Parra-Rodriguez, A., Sanz, M. & Solano, E. Digital-analog quantum simulations with superconducting circuits. Adv. Phys. X 3, 1457981 (2018).


    Google Scholar
     

  • Brydges, T. et al. Probing Rényi entanglement entropy by way of randomized measurements. Science 364, 260–263 (2019).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Kokail, C. et al. Self-verifying variational quantum simulation of lattice fashions. Nature 569, 355–360 (2019). This text experiences the demonstration of an analogue quantum simulator getting used for variational quantum simulation, demonstrating a self-verified resolution to a mannequin from high-energy physics.

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Babukhin, D. V., Zhukov, A. A. & Pogosov, W. V. Hybrid digital-analog simulation of many-body dynamics with superconducting qubits. Phys. Rev. A 101, 052337 (2020).

    CAS 
    Article 
    ADS 

    Google Scholar
     

  • Arrazola, I., Pedernales, J. S., Lamata, L. & Solano, E. Digital-analog quantum simulation of spin fashions in trapped ions. Sci. Rep. 6, 30534 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • Kokail, C., van Bijnen, R., Elben, A., Vermersch, B. & Zoller, P. Entanglement Hamiltonian tomography in quantum simulation. Nat. Phys. 17, 936–942 (2021).

  • Joshi, M. Okay. et al. Quantum info scrambling in a trapped-ion quantum simulator with tunable vary interactions. Phys. Rev. Lett. 124, 240505 (2020).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Henriet, L. et al. Quantum computing with impartial atoms. Quantum 4, 327 (2020).

    Article 

    Google Scholar
     

  • Cerezo, M. et al. Variational quantum algorithms. Nat. Rev. Phys. 3, 625–644 (2021).

  • Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum approximate optimization algorithm: efficiency, mechanism, and implementation on near-term units. Phys. Rev. X 10, 021067 (2020).

    CAS 

    Google Scholar
     

  • Biamonte, J. et al. Quantum machine studying. Nature 549, 195–202 (2017).

    CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar
     

  • Huang, H.-Y. et al. Energy of information in quantum machine studying. Nat. Commun. 12, 2631 (2021).

  • Bloch, I., Dalibard, J. & Nascimbène, S. Quantum simulations with ultracold quantum gases. Nat. Phys. 8, 267–276 (2012).

    CAS 
    Article 

    Google Scholar
     

  • Schäfer, F., Fukuhara, T., Sugawa, S., Takasu, Y. & Takahashi, Y. Instruments for quantum simulation with ultracold atoms in optical lattices. Nat. Rev. Phys. 2, 411–425 (2020).

    Article 
    CAS 

    Google Scholar
     

  • The Hubbard mannequin at half a century. Nat. Phys. 9, 523 (2013).

  • Essler, F. H. L., Frahm, H., Göhmann, F., Klümper, A. & Korepin, V. E. The One-Dimensional Hubbard Mannequin (Cambridge Univ. Press, 2005).

  • Lee, P. A., Nagaosa, N. & Wen, X.-G. Doping a Mott insulator: physics of high-temperature superconductivity. Rev. Mod. Phys. 78, 17–85 (2006).

    CAS 
    Article 
    ADS 

    Google Scholar
     

  • von Burg, V. et al. Quantum computing enhanced computational catalysis. Phys. Rev. Res. 3, 033055 (2021).

    Article 

    Google Scholar
     

  • Bauer, B., Bravyi, S., Motta, M. & Chan, G. Okay.-L. Quantum algorithms for quantum chemistry and quantum supplies science. Chem. Rev. 120, 12685–12717 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

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