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Universal Quantum Simulators
Abstract
Feynman's 1982 conjecture, that quantum computers can be programmed to simulate any local quantum system, is shown to be correct.
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In the citing paper
“The algorithm runs in time polynomial in the size of the system being simulated (the number of qubits) and the desired evolution time, giving an exponential speedup over the best general classical algorithms known.”
From Quantum algorithms: an overview (2016) · cites this paper as [44]
In the citing paper
“A general-purpose quantum computer can indeed ef fi ciently simulate quantum mechanics in this sense for many physically realistic cases, such as systems with locality restrictions on their interactions. 44”
From Quantum algorithms: an overview (2016) · cites this paper as [44]
In the citing paper
“It is noteworthy that we can program quantum computers to simulate localized quantum systems [68].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [68]
In the citing paper
“With this universal approach, a wide range of quantum problems can be simulated to the desired accuracy with a polynomial increase in quantum resources only (Lloyd, 1996).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Lloyd, 1996
In the citing paper
“The belief in this quantum approach was indeed supported in the 90's by the first efficient quantum algorithms [316], which were rapidly followed by breakthrough discoveries applicable to quantum chemistry [3,624]”
From Photonic quantum information processing: a review (2019) · cites this paper as [3]
In the citing paper
“The quantum computational cost of these algorithms has continued to be reduced 47-50 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [50]
In the citing paper
“Later, it was also demonstrated that the Trotter errors depend on the maximum nuclear charge rather than the number of spin-orbitals 50 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [50]
In the citing paper
“In the general case, it would require a number of elementary gates of 2 20 ≃ 10 6 [2]. With our method, the number of gates can be as small as 10 per Trotter step, consisting of Mølmer-Sørensen gates (dark blue and green), local exp( iφ ′ σ y 2 ) gates (red), exp[ ± i ( π/ 4) ∑ i σ y i ] (yellow) and exp[ ± i ( π/ 4) ∑ i σ x i ] (cyan) gates (see Fig. 1b) [35].”
From Quantum Simulation of Interacting Fermion Lattice Models in Trapped Ions (2012) · cites this paper as [2]
In the citing paper
“We map products of fermionic operators onto nonlocal spin operators and decompose the resulting dynamics in efficient steps with Trotter methods, yielding an overall protocol that employs only polynomial resources.”
From Quantum Simulation of Interacting Fermion Lattice Models in Trapped Ions (2012) · cites this paper as [2]
In the citing paper
“Note that for bounded Hamiltonians, the Trotter expansion associated with the exponential of the polynomial sum of efficiently implementable nonlocal terms is also efficient [2, 29, 30], i.e., it only requires polynomial resources [35].”
From Quantum Simulation of Interacting Fermion Lattice Models in Trapped Ions (2012) · cites this paper as [2]
In the citing paper
“On a fault-tolerant computer, one may directly measure the full-CI energy by first preparing a wave function ansatz on the quantum hardware (as in VQE) and then applying the Quantum Phase Estimation algorithm, which projects a prepared state into an energy eigenstate of the Hamiltonian.[111,[132][133][134][135][136]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [111]
In the citing paper
“A proposed solution is to use highly controlled laboratory quantum systems themselves to perform such simulations [1-3].”
From Quantum Simulation of the Klein Paradox with Trapped Ions (2011) · cites this paper as [2]
In the citing paper
“Simulating quantum mechanics using conventional computers rapidly becomes intractable as the physical systems to which it is applied grow larger.”
From Quantum Simulation of the Klein Paradox with Trapped Ions (2011) · cites this paper as [2]
In the citing paper
“Indeed, this fact is related to correlation and entanglement spreading according to the Lieb-Robinson theorem that states that, after a sufficiently large time, states can become strongly entangled ([91-96], see also [97]).”
From Can one trust quantum simulators? (2012) · cites this paper as [96]
In the citing paper
“or that the quantum dynamics is in general computationally hard [96, 97].”
From Can one trust quantum simulators? (2012) · cites this paper as [96]
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Items 701–800 of 1,008
Cotler, J. S., Choi, S., Lukin, A., Gharibyan, H., Grover, T., Tai, M. E., Rispoli, M., Schittko, R., Preiss, P. M., Kaufman, A. M., et al. (2019) · Physical Review X · doi:10.1103/physrevx.9.031013 · Cited by 61
“[1] Lloyd, Seth.”
BackgroundEvaluating Energy Differences on a Quantum Computer with Robust Phase Estimation
Russo, A., Rudinger, K., Morrison, B., & Baczewski, A. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.210501 · Cited by 54
Robust quantum compilation and circuit optimisation via energy minimisation
Jones, T., & Benjamin, S. C. (2022) · Quantum · doi:10.22331/q-2022-01-24-628 · Cited by 84
“A naive approach might be to use a number of Trotter ‘cy-cles’ [39, 40] i.e. to use a number q of identical circuit blocks each of which contains one gate for each Pauli term in H sys .”
MethodAlgorithms for the Optimization of Quantum Circuits
Amy, M. (2013) · UWSpace (University of Waterloo) · Cited by 54
Quantum Computing: An Overview Across the System StackPreprint
Resch, S., & Karpuzcu, U. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07240 · Cited by 94
“hemistry would scale polynomially [3, 29, 31]. This could speed up simulations that are presently being run and enable simulation of larger systems and excited energy levels. Quantum Phase estimation [32] is the quantum algorithm that would be used in a large scale, fault-tolerant quantum computer [33]. Grover’s searc…”
MethodClark, C. R., Metodi, T. S., Gasster, S. D., & Brown, K. R. (2009) · Physical Review A · doi:10.1103/physreva.79.062314 · Cited by 51
LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction
Das, P., Locharla, A., & Jones, C. (2022) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3503222.3507707 · Cited by 84
“However, fault-tolerant quantum computers can solve a broader class of problems [48, 75] and consequently, real-system QEC studies are gaining momentum [6, 68, 70] with improving device qualities and availability of quantum hardware prototypes.”
BackgroundDavoudi, Z., Shaw, A. F., & Stryker, J. R. (2023) · Quantum · doi:10.22331/q-2023-12-20-1213 · Cited by 92
Control of Trapped-Ion Quantum States with Optical Pulses
Rangan, C., Bloch, A. M., Monroe, C. R., & Bucksbaum, P. H. (2004) · Physical Review Letters · doi:10.1103/physrevlett.92.113004 · Cited by 50
Suppression of arbitrary internal coupling in a quantum register
Stollsteimer, M., & Günter Mahler, A. (2001) · Physical Review A · doi:10.1103/physreva.64.052301 · Cited by 51
Simulating Quantum Fields with Cavity QED
Barrett, S. D., Hammerer, K., Harrison, S. K., Northup, T. E., & Osborne, T. J. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.090501 · Cited by 51
Proposal for a scalable universal bosonic simulator using individually trapped ions
Lau, H.-K., & James, D. F. V. (2012) · Physical Review A · doi:10.1103/physreva.85.062329 · Cited by 52
Detecting multipartite entanglement structure with minimal resources
Zhou, Y., Zhao, Q., Yuan, X., & Ma, X. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0200-9 · Cited by 59
Variational Quantum Simulation for Quantum Chemistry
Li, Y., Hu, J., Zhang, X., Song, Z., & Yung, M. (2019) · Advanced Theory and Simulations · doi:10.1002/adts.201800182 · Cited by 56
Limitations on the simulation of non-sparse hamiltoniansPreprint
Childs, A. M., & Kothari, R. (2010) · doi:10.5555/2011373.2011380 · Cited by 56
Quantum Algorithms for Fermionic Quantum Field TheoriesPreprint
Jordan, S. P., Lee, K. S. M., & Preskill, J. P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.7115 · Cited by 77
“teria with the following definition: Qj = X x∈Ω aψ¯ j(x)γ0ψj(x) −Lˆ1. (99) By (27), (28), and (36), one finds that Qj = 1 L X p∈Γ a† j(p)aj(p)−b † j(p)bj(p) . (100) For any envelope function f: Ω → [0,1], one can similarly define Q(f) j = X x∈Ω f(x) aψ¯ j(x)γ0ψj(x) −1 . (101) If fhas support only in some region R⊂ Ω, the…”
Analysis of Quantum Error-Correcting Codes: Symplectic Lattice Codes and Toric Codes
Harrington, J. W. (2004) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/ahmq-eg82 · Cited by 54
Optimal control for the quantum simulation of nuclear dynamics
Holland, E. T., Wendt, K., Kravvaris, K., Wu, X., Ormand, W. E., DuBois, J. L., Quaglioni, S., & Pederiva, F. (2020) · Physical Review A · doi:10.1103/physreva.101.062307 · Cited by 55
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Ajoy, A., & Cappellaro, P. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.220503 · Cited by 53
Floating point representations in quantum circuit synthesis
Wiebe, N., & Kliuchnikov, V. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/9/093041 · Cited by 53
“[1] Seth Lloyd.”
MethodDigital atom interferometer with single particle control on a discretized space-time geometry
Steffen, A., Alberti, A., Alt, W., Belmechri, N., Hild, S. J., Karski, M., Widera, A., & Meschede, D. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1204285109 · Cited by 50
“In the quest for quantum devices functioning at the singleatom level, from Rydberg quantum logic gates (20, 21) to singlesite-resolved optical lattices (22–24), our results lay the base for digital quantum simulators (25) and quantum cellular automata (9, 26) based on interacting atoms in lattice potentials (27, 28).”
BackgroundFew-qubit quantum-classical simulation of strongly correlated lattice fermions
Kreula, J. M., García-Álvarez, L., Lamata, L., Clark, S. R., Solano, E., & Jaksch, D. (2016) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-016-0049-1 · Cited by 56
“Digital simulation approaches, akin to universal quantum simulators [22], have started to emerge in recent years, for example based on superconducting circuits [23, 24, 25, 26].”
BackgroundCompilation by stochastic Hamiltonian sparsification
Ouyang, Y., White, D. R., & Campbell, E. T. (2020) · Quantum · doi:10.22331/q-2020-02-27-235 · Cited by 66
“Using quantum mechanics to simulate quantum systems [6] will provide unprecedented detail in the solution to chemical problems, enabling us to better understand the dynamics of highly entangled systems, predicting their properties and chemical reactions [1, 7].”
BackgroundKreshchuk, M., Jia, S., Kirby, W. M., Goldstein, G. R., Vary, J. P., & Love, P. J. (2021) · Physical Review A · doi:10.1103/physreva.103.062601 · Cited by 62
“The standard approach to quantum simulation of Hamiltonian dynamics of QFTs is as follows [52]: a) initialize the system in a certain state of the free Hamiltonian, b) adiabatically turn on the interaction, c) if necessary, evolve the system with time, and d) measure the energy (or another observable) of the system us…”
MethodIntroduction to Quantum Algorithms for Physics and Chemistry
Yung, M. H., Whitfield, J. D., Boixo, S., Tempel, D. G., & Aspuru‐Guzik, A. (2014) · Advances in chemical physics · doi:10.1002/9781118742631.ch03 · Cited by 60
“This conjecture was justified by Lloyd (1996), who argued that any Hamiltonian which can be decomposed into m local terms { H i } can be simulated efficiently by a universal quantum computer.”
BackgroundMethodIntroduction to quantum algorithms
Shor, P. W. (2002) · Proceedings of symposia in applied mathematics · doi:10.1090/psapm/058/1922896 · Cited by 55
“Some work has been done on simulating Hamiltonians on quantum computers [1, 29, 45], but I do not believe this question has been completely addressed yet.”
Scaling and logic in the colour code on a superconducting quantum processor
Lacroix, N., Bourassa, A., Heras, F. J. H., Zhang, L. M., Bausch, J., Senior, A. W., Edlich, T., Shutty, N., Sivak, V., Bengtsson, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09061-4 · Cited by 51
Combinatorial optimization via highly efficient quantum walks
Marsh, S., & Wang, J. B. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023302 · Cited by 64
Quantum computing with and for many-body physics
Ayral, T., Besserve, P., Denis Lacroix, A., & Guzman, E. A. R. (2023) · The European Physical Journal A · doi:10.1140/epja/s10050-023-01141-1 · Cited by 71
“The so-called first-order Trotter-Suzuki formula [145] reads”
BackgroundMethodEvidence of Kardar-Parisi-Zhang scaling on a digital quantum simulator
Keenan, N., Robertson, N., Murphy, T., Zhuk, S. M., & Goold, J. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00742-4 · Cited by 66
Quantum complexity in gravity, quantum field theory, and quantum information science
Baiguera, S., Balasubramanian, V., Caputa, P., Chapman, S., Haferkamp, J., Heller, M. P., & Halpern, N. Y. (2025) · Physics Reports · doi:10.1016/j.physrep.2025.11.001 · Cited by 57
Experimental Realization of a Compressed Quantum Simulation of a 32-Spin Ising Chain
Li, Z., Zhou, H., Ju, C., Chen, H., Zheng, W., Lu, D., Rong, X., Duan, C., Peng, X., & Du, J. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.220501 · Cited by 48
Ordering of Trotterization: Impact on Errors in Quantum Simulation of Electronic Structure
Tranter, A., Love, P. J., Mintert, F., Wiebe, N., & Coveney, P. V. (2019) · Entropy · doi:10.3390/e21121218 · Cited by 58
Simulating Hamiltonians in quantum networks: Efficient schemes and complexity bounds
Wocjan, P., Rötteler, M., Janzing, D., & Beth, T. (2002) · Physical Review A · doi:10.1103/physreva.65.042309 · Cited by 50
“Various schemes for constructing gate sequences which simulate the unitary evolution corresponding to a given Hamiltonian have been suggested (see e. g. [18, 23]).”
BackgroundQubit Control Limited by Spin–Lattice Relaxation in a Nuclear Spin-Free Iron(III) Complex
Zadrozny, J. M., & Freedman, D. E. (2015) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.5b02429 · Cited by 51
Quantum computation of molecular response properties
Cai, X., Fang, W., Fan, H., & Li, Z. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033324 · Cited by 51
“Initially advocated by Feynman [11], quantum computation shows great promise for solving interacting fermion problems in physics and chemistry [12–19].”
BackgroundHonda, M., Itou, E., Kikuchi, Y., Nagano, L., & Okuda, T. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.014504 · Cited by 55
“For fixed T , this approximation results in the error of O(δt2) for the whole operator [77, 78].”
BackgroundAvoiding barren plateaus with classical deep neural networks
Friedrich, L., & Maziero, J. (2022) · Physical Review A · doi:10.1103/physreva.106.042433 · Cited by 49
“It is expected that several areas of knowledge will benefit from these devices, such as for example the simulation of quantum systems [1], the solu-tion of linear systems of equations [2], natural language processing [3, 4], and the discovery of new drugs [5].”
BackgroundLinear-depth quantum circuits for multiqubit controlled gates
da Silva, A. J., & Park, D. K. (2022) · Physical Review A · doi:10.1103/physreva.106.042602 · Cited by 51
Quantum simulation of conical intersections using trapped ions
Whitlow, J., Jia, Z., Wang, Y., Fang, C., Kim, J., & Brown, K. R. (2023) · Nature Chemistry · doi:10.1038/s41557-023-01303-0 · Cited by 58
“Quantum simulators do not run into the scaling problems that classical computers experience when performing chemical calculations 2,11,12 .”
BackgroundStabilizer entropy dynamics after a quantum quench
Rattacaso, D., Leone, L., Oliviero, S. F. E., & Hamma, A. (2023) · Physical Review A · doi:10.1103/physreva.108.042407 · Cited by 67
Towards practical classical processing for the surface code: Timing analysis
Fowler, A. G., Whiteside, A. C., & Hollenberg, L. C. L. (2012) · Physical Review A · doi:10.1103/physreva.86.042313 · Cited by 49
Arrow of time and its reversal on the IBM quantum computer
Lesovik, G. B., Sadovskyy, I., Suslov, M. V., Lebedev, A. V., & Vinokur, V. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-40765-6 · Cited by 57
Realization of efficient quantum gates with a superconducting qubit-qutrit circuit
Bækkegaard, T., Kristensen, L. B., Loft, N. J. S., Andersen, C. K., Petrosyan, D., & Zinner, N. T. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000366525 · Cited by 48
Primitive quantum gates for dihedral gauge theories
Alam, M. S., Hadfield, S., Lamm, H., & Li, A. C. Y. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.114501 · Cited by 49
Quantum Simulation of Dissipative Collective Effects on Noisy Quantum Computers
Cattaneo, M., Rossi, M. A. C., García-Pérez, G., Zambrini, R., & Maniscalco, S. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.010324 · Cited by 54
“Note that the latter step amounts to simulating the closed-system dynamics driven by the Hamiltonian HS , which is a well-known task in quantum computing since the seminal paper by Lloyd [3].”
BackgroundInfluentialExperimental Observation of Thermalization with Noncommuting Charges
Kranzl, F., Lasek, A., Joshi, M. K., Kalev, A., Blatt, R., Roos, C. F., & Halpern, N. Y. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.020318 · Cited by 58
“We perform this cycle, or Trotter step , N T times [76].”
Observation of constructive interference at the edge of quantum ergodicity
Collaborators, G. Q. A. A., Abanin, D. A., Acharya, R., Beni, L. A., Aigeldinger, G., Ajoy, A., Alcaraz, R., Aleǐner, I. L., Andersen, T. I., Ansmann, M., et al. (2025) · Nature · doi:10.1038/s41586-025-09526-6 · Cited by 71
Manousakis, E. (2002) · Journal of Low Temperature Physics · doi:10.1023/a:1014295416763 · Cited by 48
Quantum simulation of the hexagonal Kitaev model with trapped ions
Schmied, R., Wesenberg, J. H., & Leibfried, D. G. (2011) · New Journal of Physics · doi:10.1088/1367-2630/13/11/115011 · Cited by 48
Olson, J. P., Seshadreesan, K. P., Motes, K. R., Rohde, P. P., & Dowling, J. P. (2015) · Physical Review A · doi:10.1103/physreva.91.022317 · Cited by 50
Lenz, S., Bader, K., Bamberger, H., & van Slageren, J. (2017) · Chemical Communications · doi:10.1039/c6cc07813c · Cited by 49
Floquet Quantum Simulation with Superconducting Qubits
Kyriienko, O., & Sørensen, A. S. (2018) · Physical Review Applied · doi:10.1103/physrevapplied.9.064029 · Cited by 48
Emergence of classical objectivity of quantum Darwinism in a photonic quantum simulator
Chen, M.-C., Zhong, H.-S., Li, Y., Wu, D., Wang, X., Li, L., Liu, N.-L., Lu, C., & Pan, J.-W. (2019) · 中国科学通报：英文版 · doi:10.1016/j.scib.2019.03.032 · Cited by 50
Quantum Kibble-Zurek physics in long-range transverse-field Ising models
Puebla, R., Marty, O., & Plenio, M. B. (2019) · Physical Review A · doi:10.1103/physreva.100.032115 · Cited by 51
Topological superfluid transition in bubble-trapped condensates
Tononi, A., Pelster, A., & Salasnich, L. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013122 · Cited by 51
“These basic concepts are at the core of Feynman’s visionary idea [1] of simulating the complex behavior of many-body systems with controllable algorithms that, rather than being implemented in classical computers, are based on the laws of quantum mechanics [2, 3].”
BackgroundQuantum algorithms for quantum dynamics: A performance study on the spin-boson model
Miessen, A., Ollitrault, P. J., & Tavernelli, I. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043212 · Cited by 51
Hybridized Methods for Quantum Simulation in the Interaction Picture
Rajput, A., Roggero, A., & Wiebe, N. (2022) · Quantum · doi:10.22331/q-2022-08-17-780 · Cited by 62
“The first, and most space efficient, algorithms are the Trotter-Suzuki formulas and their time-ordered generalizations [2, 7, 8, 9, 10], but recent years have seen several additions to the repertoire of quantum simulation techniques.”
BackgroundMethodRobust resource-efficient quantum variational ansatz through an evolutionary algorithm
Huang, Y., Li, Q., Hou, X., Wu, R.-B., Yung, M., Bayat, A., & Wang, X. (2022) · Physical Review A · doi:10.1103/physreva.105.052414 · Cited by 56
“Thanks to recent technological advances, quantum devices are rapidly scaling up in size with improved performance, significantly increasing the possibility of realizing true quantum simulations [1–4].”
BackgroundEfficient quantum algorithm for preparing molecular-system-like states on a quantum computer
Wang, H., Ashhab, S., & Nori, F. (2009) · Physical Review A · doi:10.1103/physreva.79.042335 · Cited by 48
A biomolecular implementation of logically reversible computation with minimal energy dissipation
Klein, J. P., Leete, T. H., & Rubin, H. N. (1999) · Biosystems · doi:10.1016/s0303-2647(99)00028-3 · Cited by 48
Lloyd, S., Landahl, A. J., & Slotine, J.-J. E. (2004) · Physical Review A · doi:10.1103/physreva.69.012305 · Cited by 46
Self-citationApplications of Electromagnetically Induced Transparency to Quantum Information Processing
Beausoleil, R. G., Munro, W. J., Rodrigues, D. A., & Spiller, T. P. (2004) · CERN Document Server (European Organization for Nuclear Research) · Cited by 48
Holographic dynamics simulations with a trapped-ion quantum computer
Chertkov, E., Bohnet, J., Francois, D., Gaebler, J., Gresh, D., Hankin, A., Lee, K., Hayes, D., Neyenhuis, B., Stutz, R., et al. (2022) · Nature Physics · doi:10.1038/s41567-022-01689-7 · Cited by 53
Quantum computation: Algorithms and Applications
Cho, C., Chen, C.-Y., Chen, K.-C., Huang, T., Hsu, M.-C., Cao, N., Zeng, B., Tan, S.-G., & Chang, C. (2021) · Chinese Journal of Physics · doi:10.1016/j.cjph.2021.05.001 · Cited by 54
Hamiltonian Simulation with Random Inputs
Zhao, Q., Zhou, Y., Shaw, A. F., Li, T., & Childs, A. M. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.270502 · Cited by 48
Quantum state preparation and nonunitary evolution with diagonal operators
Schlimgen, A. W., Head-Marsden, K., Sager-Smith, L. M., Narang, P., & Mazziotti, D. A. (2022) · Physical Review A · doi:10.1103/physreva.106.022414 · Cited by 52
Liu, Y., Singh, S., Smith, K. C., Crane, E., Martyn, J. M., Eickbusch, A., Schuckert, A., Li, R. D., Sinanan-Singh, J., Soley, M. B., et al. (2025) · PRX Quantum · doi:10.1103/4rf7-9tfx · Cited by 107
Loss in hybrid qubit-bus couplings and gates
Louis, S. G. R., Munro, W. J., Spiller, T. P., & Nemoto, K. (2008) · Physical Review A · doi:10.1103/physreva.78.022326 · Cited by 46
Noise-Resilient Quantum Computing with a Nitrogen-Vacancy Center and Nuclear Spins
Casanova, J., Wang, Z.-Y., & Plenio, M. B. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.130502 · Cited by 48
Gross–Neveu–Wilson model and correlated symmetry-protected topological phases
Bermúdez, A., Tirrito, E., Rizzi, M., Lewenstein, M., & Hands, S. (2018) · Annals of Physics · doi:10.1016/j.aop.2018.10.007 · Cited by 42
Toward quantum simulations of Z 2 gauge theory without state preparation
Gustafson, E., & Lamm, H. (2021) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.103.054507 · Cited by 46
“stroboscopic set of operators, alternating between even and odd site spins [58]:”
BackgroundHigh-speed quantum gates with cavity quantum electrodynamics
Su, C., Greentree, A. D., Munro, W. J., Nemoto, K., & Hollenberg, L. C. L. (2008) · Physical Review A · doi:10.1103/physreva.78.062336 · Cited by 45
Das, R., Mitra, A., Kumar, S., & Kumar, A. (2003) · International Journal of Quantum Information · doi:10.1142/s0219749903000292 · Cited by 47
Experimental Simulation of Open Quantum System Dynamics via Trotterization
Han, J., Cai, W., Hu, L., Mu, X. D., Ma, Y., Xu, Y., Wang, W., Wang, H., Song, Y., Zou, C., et al. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.020504 · Cited by 51
Bounds in nonequilibrium quantum dynamics
Gong, Z., & Hamazaki, R. (2022) · International Journal of Modern Physics B · doi:10.1142/s0217979222300079 · Cited by 59
Transient time of an Ising machine based on injection-locked laser network
Takata, K., Utsunomiya, S., & Yamamoto, Y. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/1/013052 · Cited by 45
Quantum Simulation of Small-Polaron Formation with Trapped Ions
Stojanović, V. M., Shi, T., Bruder, C., & Cirac, J. I. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.250501 · Cited by 43
Long-range coupling and scalable architecture for superconducting flux qubits
Fowler, A. G., Thompson, W. F., Yan, Z., Stephens, A. M., Plourde, B. L. T., & Wilhelm, F. K. (2007) · Physical Review B · doi:10.1103/physrevb.76.174507 · Cited by 46
Digital-analog quantum simulation of generalized Dicke models with superconducting circuits
Lamata, L. (2017) · Scientific Reports · doi:10.1038/srep43768 · Cited by 48
“∑ ε = < H H H t n ( ) [ , ] 2 , (14) i i j i j 2”
Efficient universal quantum channel simulation in IBM’s cloud quantum computer
Wei, S.-J., Xin, T., & Long, G. (2018) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-017-9181-9 · Cited by 46
Multilevel 2-D Quantum Wavelet Transforms
Li, H.-S., Fan, P., Peng, H., Song, S., & Long, G. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3049509 · Cited by 46
Nearly tight Trotterization of interacting electrons
Su, Y., Huang, H.-Y., & Campbell, E. T. (2020) · CaltechAUTHORS (California Institute of Technology) · Cited by 49
Experimental coherent computation of a multiple-input AND gate using pure molecular superpositions
Amitay, Z., Kosloff, R., & Leone, S. R. (2002) · Chemical Physics Letters · doi:10.1016/s0009-2614(02)00551-1 · Cited by 46
Dynamical mean field theory algorithm and experiment on quantum computersPreprint
Rungger, I., Fitzpatrick, N., Chen, H., Alderete, C. H., Apel, H., Cowtan, A., Patterson, A., Ramo, D. M., Zhu, Y., Nguyen, N. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04735 · Cited by 73
“[20] S.”
MethodComputing Spin NetworksPreprint
Marzuoli, A., & Rasetti, M. (2004) · Cited by 49
Perspectives of quantum computing for chemical engineering
Bernal, D. E., Ajagekar, A., Harwood, S. M., Stober, S. T., Trenev, D., & You, F. (2022) · AIChE Journal · doi:10.1002/aic.17651 · Cited by 51
Realizing quantum gates with optically addressable Yb + 171 ion qudits
Aksenov, M. A., Zalivako, I. V., Semerikov, I. A., Borisenko, A. S., Semenin, N. V., Sidorov, P., Fedorov, A. K., Khabarova, K. Y., & Kolachevsky, N. (2023) · Physical Review A · doi:10.1103/physreva.107.052612 · Cited by 51
Dynamical simulation via quantum machine learning with provable generalization
Gibbs, J., Holmes, Z., Caro, M. C., Ezzell, N., Huang, H.-Y., Cincio, Ł., Sornborger, A. T., & Coles, P. J. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013241 · Cited by 55
“Introduction The exponential speedup of dynamical quantum simulation provided the original motivation for quantum computers [1, 2].”
BackgroundDelocalized-localized transition in a semiconductor two-dimensional honeycomb lattice
De Simoni, G., Singha, A., Gibertini, M., Karmakar, B., Polini, M., Piazza, V., Pfeiffer, L. N., West, K. W., Beltram, F., & Pellegrini, V. (2010) · Applied Physics Letters · doi:10.1063/1.3493189 · Cited by 45
Implementing and Characterizing Precise Multiqubit Measurements
Blumoff, J. Z., Chou, K., Shen, C., Reagor, M., Axline, C. J., Brierley, R. T., Silveri, M., Wang, C., Vlastakis, B., Nigg, S. E., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031041 · Cited by 47
“Building on impressive progress in control [1,2], measurement [3,4], and coherence [5,6], experimental quantum information science is addressing increasingly complex challenges, such as quantum error correction [7–12] and quantum simulation [13,14].”
Quantum simulation of quantum channels in nuclear magnetic resonance
Xin, T., Wei, S.-J., Pedernales, J. S., Solano, E., & Long, G. (2017) · Physical Review A · doi:10.1103/physreva.96.062303 · Cited by 45
Experimental demonstration of a digital quantum simulation of a general PT -symmetric system
Wen, J., Zheng, C., Kong, X., Wei, S., Xin, T., & Long, G. (2019) · Physical Review A · doi:10.1103/physreva.99.062122 · Cited by 45
Fast-forwarding quantum evolution
Gu, S., Somma, R. D., & Şahinoğlu, B. (2021) · Quantum · doi:10.22331/q-2021-11-15-577 · Cited by 45
“, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]).”
Berthusen, N., Trevisan, T. V., Iadecola, T., & Orth, P. P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023097 · Cited by 52
“The maximal value of τ in a single compression step is determined by the number of Trotter steps nmax that can be executed on the noisy QPU, and by the desired Trotter error threshold ε, which is a function of τ/nmax [4, 36].”
BackgroundMethodUniversal simulation of Hamiltonians using a finite set of control operations
Wocjan, P., Rötteler, M., Janzing, D., & Beth, T. (2002) · Quantum Information and Computation · doi:10.26421/qic2.2-3 · Cited by 54
Error correction optimisation in the presence of X/Z asymmetryPreprint
Evans, Z. W. E., Stephens, A. M., Cole, J. H., & Hollenberg, L. C. L. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0709.3875 · Cited by 50
MoG-VQE: Multiobjective genetic variational quantum eigensolverPreprint
Сергеевич, Ч. Д., Samarin, A., Игоревич, У. В., Iorsh, I. V., Оганов, А. Р., & Kyriienko, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04424 · Cited by 108
“By using quantum phase estimation and Hamiltonian simulation techniques [5, 6], one can perform calculations with exponentially improved scaling as compared to classical computational methods.”
MethodQuantum spacetime on a quantum simulator
Li, K., Li, Y., Han, M., Lu, S., Zhou, J., Ruan, D., Long, G., Wan, Y., Lu, D., Zeng, B., et al. (2019) · Communications Physics · doi:10.1038/s42005-019-0218-5 · Cited by 53
Metadata
- Authors
- Lloyd, S.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Lloyd, Seth
- ORCID
- Lloyd, Seth
- Venue
- Science
- Published
- 1996-08-23
- Volume
- 273
- Issue
- 5278
- Pages
- 1073-1078
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2040792108
- Cited by
- 3,088
- References
- 54
- Citations by year
- 2026: 183 · 2025: 317 · 2024: 287 · 2023: 268 · 2022: 221 · 2021: 251 · 2020: 208 · 2019: 150 · 2018: 117 · 2017: 134 · 2016: 104 · 2015: 82 · 2014: 97 · 2013: 72 · 2012: 75
- FWCI
- 30.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- quantum simulation, quantum computing hardware, quantum computing
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