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Solving strongly correlated electron models on a quantum computer
Open accessAbstract
One of the main applications of future quantum computers will be the simulation of quantum models. While the evolution of a quantum state under a Hamiltonian is straightforward (if sometimes expensive), using quantum computers to determine the ground state phase diagram of a quantum model and the properties of its phases is more involved. Using the Hubbard model as a prototypical example, we here show all the steps necessary to determine its phase diagram and ground state properties on a quantum computer. In particular, we discuss strategies for efficiently determining and preparing the ground state of the Hubbard model starting from various mean-field states with broken symmetry. We present an efficient procedure to prepare arbitrary Slater determinants as initial states and present the complete set of quantum circuits needed to evolve from these to the ground state of the Hubbard model. We show that, using efficient nesting of the various terms each time step in the evolution can be performed with just $\mathcal{O}(N)$ gates and $\mathcal{O}(\log N)$ circuit depth. We give explicit circuits to measure arbitrary local observables and static and dynamic correlation functions, both in the time and frequency domain. We further present efficient non-destructive approaches to measurement that avoid the need to re-prepare the ground state after each measurement and that quadratically reduce the measurement error.
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“In the long term, quantum simulation using quantum computers has great potential to wield a broad influence on the world [48, 49, 50].”
From Quantum Computing in the NISQ era and beyond (2018) · cites this paper as [49]
Experimental — accuracy not yet measuredIn the citing paper
“The cost of estimating the correct ground state energy depends directly on the overlap of the initial state with the ground state, and it becomes progressively more expensive as the overlap with the correct ground state decreases [20, 48–50].”
From Drug design on quantum computers (2024) · cites this paper as [48]
In the citing paper
“Even though the run-time has improved over time [48, 49], the dependence on the overlap of the initial and target states cannot be circumvented [50].”
From Drug design on quantum computers (2024) · cites this paper as [48]
In the citing paper
“Correspondingly, most of the experimental implementations so far have employed methods that better tolerate these fluctuations, such as Nelder-Mead and Particle-Swarm Optimization (PSO) 353 , which are gradient free. More recently the Simultaneous Perturbation Stochastic Approximation (SPSA) has been applied 354,355 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [354]
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Cites
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691
The Resonating Valence Bond State in La 2 CuO 4 and Superconductivity
Anderson, P. W. (1987) · Science · doi:10.1126/science.235.4793.1196 · Cited by 7,666
Electron correlations in narrow energy bands
Hubbard, J. (1963) · Proceedings of the Royal Society of London A Mathematical and Physical Sciences · doi:10.1098/rspa.1963.0204 · Cited by 6,687
Resonating valence bonds: A new kind of insulator?
Anderson, P. W. (1973) · Materials Research Bulletin · doi:10.1016/0025-5408(73)90167-0 · Cited by 2,852
Sachdev, S. (2011) · doi:10.1017/cbo9780511973765 · Cited by 2,842
On the product of semi-groups of operators
Trotter, H. F. (1959) · Proceedings of the American Mathematical Society · doi:10.1090/s0002-9939-1959-0108732-6 · Cited by 2,282
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., & Preda, D. (2001) · Science · doi:10.1126/science.1057726 · Cited by 2,056
Effect of Correlation on the Ferromagnetism of Transition Metals
Gutzwiller, M. C. (1963) · Physical Review Letters · doi:10.1103/physrevlett.10.159 · Cited by 1,905
Research works printed but not linked: 2
- M. B. Hastings, 2015, Quantum Inf. Comput., 15, 1
- D. Poulin, 2015, Quantum Inf. Comput., 15, 361
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Quantum Chemistry in the Age of Quantum Computing
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Collaborators, G. A. Q. A., Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Boixo, S., Broughton, M., Buckley, B. B., et al. (2020) · Science · doi:10.1126/science.abb9811 · Cited by 870
Self-citationElucidating reaction mechanisms on quantum computers
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619152114 · Cited by 745
Self-citationPractical quantum advantage in quantum simulation
Daley, A. J., Bloch, I., Kokail, C., Flannigan, S., Pearson, N., Troyer, M., & Zoller, P. (2022) · Nature · doi:10.1038/s41586-022-04940-6 · Cited by 783
Self-citationToward the first quantum simulation with quantum speedup
Childs, A. M., Maslov, D., Nam, Y., Ross, N. J., & Su, Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801723115 · Cited by 551
Theory of Trotter Error with Commutator Scaling
Childs, A. M., Su, Y., Tran, M. C., Wiebe, N., & Zhu, S. (2021) · Physical Review X · doi:10.1103/physrevx.11.011020 · Cited by 646
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McClean, J. R., Rubin, N. C., Sung, K. J., Kivlichan, I., Bonet-Monroig, X., Cao, Y., Dai, C., Fried, E., Gidney, C., Gimby, B., et al. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8ebc · Cited by 621
Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
Kivlichan, I. D., McClean, J., Wiebe, N., Gidney, C., Aspuru-Guzik, A., Chan, G. K.-L., & Babbush, R. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.110501 · Cited by 431
Self-citationThe Hubbard Model: A Computational Perspective
Qin, M., Schäfer, T., Andergassen, S., Corboz, P., & Gull, E. (2021) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-090921-033948 · Cited by 469
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
Babbush, R., Gidney, C., Berry, D. W., Wiebe, N., McClean, J., Paler, A., Fowler, A., & Neven, H. (2018) · Physical Review X · doi:10.1103/physrevx.8.041015 · Cited by 383
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Bauer, C., Davoudi, Z., Balantekin, A. B., Tanmoy Bhattacharya, A., Carena, M. S., de Jong, W. A., Draper, P., El-Khadra, A. X., Gemelke, N., Hanada, M., et al. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.027001 · Cited by 503
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Alexeev, Y., Bacon, D., Brown, K. R., Calderbank, R., Carr, L. D., Chong, F. T., DeMarco, B., Englund, D. R., Farhi, E., Fefferman, B., et al. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.017001 · Cited by 339
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Bauer, B., Wecker, D., Millis, A. J., Hastings, M. B., & Troyer, M. (2016) · Physical Review X · doi:10.1103/physrevx.6.031045 · Cited by 277
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Alexeev, Y., Bacon, D., Brown, K. R., Calderbank, R., Carr, L. D., Chong, F. T., DeMarco, B., Englund, D., Farhi, E., Fefferman, B., et al. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.6082/h7fqg-6vt65 · Cited by 257
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Steinbrecher, G. R., Olson, J. P., Englund, D. R., & Carolan, J. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0174-7 · Cited by 226
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Blunt, N. S., Camps, J., Crawford, O., Izsák, R., Leontica, S., Mirani, A., Moylett, A. E., Scivier, S. A., Sünderhauf, C., Schopf, P., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00574 · Cited by 192
NISQ computing: where are we and where do we go?
Lau, J. W. Z., Lim, K. H., Shrotriya, H., & Kwek, L. C. (2022) · AAPPS bulletin · doi:10.1007/s43673-022-00058-z · Cited by 199
Low rank representations for quantum simulation of electronic structure
Motta, M., Ye, E., McClean, J. R., Li, Z., Minnich, A. J., Babbush, R., & Chan, G. K. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00416-z · Cited by 207
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Häner, T., Steiger, D. S., Svore, K. M., & Troyer, M. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aaa5cc · Cited by 171
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(2024) · doi:10.1038/s41567-024-02411-5 · Cited by 132
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Dallaire-Demers, P.-L., Romero, J., Veis, L., Sim, S., & Aspuru-Guzik, A. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/ab3951 · Cited by 119
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Smelyanskiy, M., Sawaya, N. P. D., & Aspuru‐Guzik, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07195 · Cited by 218
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Linke, N. M., Johri, S., Figgatt, C., Landsman, K. A., Matsuura, A. Y., & Monroe, C. R. (2018) · Physical Review A · doi:10.1103/physreva.98.052334 · Cited by 115
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Havlíček, V., Troyer, M., & Whitfield, J. D. (2017) · Physical Review A · doi:10.1103/physreva.95.032332 · Cited by 92
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Endo, S., Kurata, I., & Nakagawa, Y. O. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033281 · Cited by 106
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Roggero, A., Li, A. C. Y., Carlson, J., Gupta, R., & Perdue, G. (2020) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.101.074038 · Cited by 103
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Macridin, A., Spentzouris, P., Amundson, J. F., & Harnik, R. (2018) · Physical Review A · doi:10.1103/physreva.98.042312 · Cited by 94
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Bravyi, S., & Gosset, D. (2017) · Communications in Mathematical Physics · doi:10.1007/s00220-017-2976-9 · Cited by 106
Quantum-optimal-control-inspired ansatz for variational quantum algorithms
Choquette, A., Di Paolo, A., Barkoutsos, P. K., Sénéchal, D., Tavernelli, I., & Blais, A. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.023092 · Cited by 86
Babbush, R., Berry, D. W., Sanders, Y. R., Kivlichan, I., Scherer, A., Wei, A. Y., Love, P. J., & Aspuru‐Guzik, A. (2017) · Quantum Science and Technology · doi:10.1088/2058-9565/aa9463 · Cited by 85
Jiang, Z., Kalev, A., Mruczkiewicz, W., & Neven, H. (2020) · Quantum · doi:10.22331/q-2020-06-04-276 · Cited by 110
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Johri, S., Steiger, D. S., & Troyer, M. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.96.195136 · Cited by 65
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Zhang, J., McClean, J. R., Babbush, R., & Neven, H. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.12.064041 · Cited by 64
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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
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Metadata
- Authors
- Wecker, D., Hastings, M. B., Wiebe, N., Clark, B. K., Nayak, C., & Troyer, M.
- Affiliations
- Microsoft (United States) (US)
- ETH Zurich (CH)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2015-12-10
- Volume
- 92
- Issue
- 6
- ISSN
- 0556-2791 (ISSN-L)
- 1050-2947
- 1094-1622
- OpenAlex
- W2167110831
- Cited by
- 285
- References
- 52
- Citations by year
- 2026: 12 · 2025: 41 · 2024: 31 · 2023: 40 · 2022: 39 · 2021: 36 · 2020: 34 · 2019: 16 · 2018: 20 · 2017: 9 · 2016: 7
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1066293)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- European Research Council (SIMCOFE)
- Aspen Center for Physics
- 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 › Cold Atom Physics and Bose-Einstein Condensates
- Keywords
- Hubbard model, strongly correlated electrons, quantum simulation, ground state preparation, phase diagram, Slater determinants, quantum circuits, circuit depth, correlation functions, non-destructive measurement, measurement error reduction, quantum computing
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