The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Simulation of electronic structure Hamiltonians using quantum computers
Open accessAbstract
Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to advance dramatically. However, the lack of computationally efficient methods for the exact simulation of quantum systems on classical computers presents a limitation of current computational approaches. We report, in detail, how a set of pre-computed molecular integrals can be used to explicitly create a quantum circuit, i.e. a sequence of elementary quantum operations, that, when run on a quantum computer, obtains the energy of a molecular system with fixed nuclear geometry using the quantum phase estimation algorithm. We extend several known results related to this idea and discuss the adiabatic state preparation procedure for preparing the input states used in the algorithm. With current and near future quantum devices in mind, we provide a complete example using the hydrogen molecule of how a chemical Hamiltonian can be simulated using a quantum computer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The time the quantum computer must remain coherent is determined by the necessity of O(p−1) successive applications of , each of which can require on the order of millions or billions of quantum gates for practical applications1719, as compared to the tens to hundreds of gates achievable in the short term.”
From A variational eigenvalue solver on a photonic quantum processor (2014) · cites this paper as [17]
Experimental — accuracy not yet measuredIn the citing paper
“Indeed, there are already proposals 200 for NISQ algorithms based on variational principles 201,202 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [201]
In the citing paper
“It is remarkable that the standard quantum-circuit treatment (e.g. see Ref.47) for implementing each involves as many as 2N two-qubit gates for simulating N fermionic modes; in our protocol one needs only two Mølmer-Sørensen gates, which are straightforwardly implementable with current trapped-ion technology.”
From From transistor to trapped-ion computers for quantum chemistry (2014) · cites this paper as [47]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O’Brien, J. L. (2010) · Nature · doi:10.1038/nature08812 · Cited by 3,592
Solution of the Schrödinger equation by a spectral method
Feit, M. D., Fleck, J. A., & Steiger, A. (1982) · Journal of Computational Physics · doi:10.1016/0021-9991(82)90091-2 · Cited by 3,375
Basis Set Exchange: A Community Database for Computational Sciences
Schuchardt, K. L., Didier, B., Elsethagen, T., Sun, L., Gurumoorthi, V., Chase, J. M., Li, J., & Windus, T. L. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600510j · Cited by 3,167
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088
The role of databases in support of computational chemistry calculations
Feller, D. (1996) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199610)17:13 · Cited by 2,713
�ber das Paulische �quivalenzverbot
Jordan, P., & Wigner, E. (1928) · Zeitschrift f�r Physik · doi:10.1007/bf01331938 · Cited by 2,597
Molecular Electronic‐Structure Theory
Helgaker, T., Jørgensen, P., & Olsen, J. (2000) · doi:10.1002/9781119019572 · Cited by 2,594
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
The semiclassical way to molecular spectroscopy
Heller, E. J. (1981) · Accounts of Chemical Research · doi:10.1021/ar00072a002 · Cited by 1,486
Quantum computations: algorithms and error correction
Kitaev, A. Y. (1997) · Russian Mathematical Surveys · doi:10.1070/rm1997v052n06abeh002155 · Cited by 1,424
Research works printed but not linked: 5
- Szabo A, 1996, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory
- Nielsen M, 2001, Quantum Computation and Quantum Information
- Boixo S, 2009, Quantum Inf. Comput., 9, 0833
- Oliveira R, 2008, Quantum Inf. Comput., 8, 0900
- Muller RP, 2007, Python Quantum Chemistry (PyQuante) program, version 1.6
Cited by
Sorted by citations
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552
Self-citationThe theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227
Self-citationQuantum computational chemistry
McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.015003 · Cited by 1,621
Self-citationQuantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560
Self-citationQuantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265
Self-citationAn adaptive variational algorithm for exact molecular simulations on a quantum computer
Grimsley, H. R., Economou, S. E., Barnes, E., & Mayhall, N. J. (2019) · Nature Communications · doi:10.1038/s41467-019-10988-2 · Cited by 1,033
“This was followed by several theoretical studies on VQE [7, 11–17] and demonstrations on other hardware such as superconducting qubits [7, 16, 18] and trapped ions [19, 20].”
MethodQuantum information processing with superconducting circuits: a review
Wendin, G. P. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa7e1a · Cited by 1,022
“A detailed analysis [389] shows that only the terms σxσxσyσy, σyσyσxσx, σyσxσxσy, σxσyσyσx need to be considered.”
MethodInfluentialAspuru-Guzik, A., & Walther, P. (2012) · Nature Physics · doi:10.1038/nphys2253 · Cited by 952
“In 2005, a quantum chemistry algorithm was introduced 28 that is linear scaling in the number of qubits and fifth-order scaling in terms of the number of quantum gates 32 .”
Self-citationQuantum Algorithms for Quantum Chemistry and Quantum Materials Science
Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00829 · Cited by 848
Scalable Quantum Simulation of Molecular Energies
O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059
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
“Conventional circuits, such as those in [13], break these terms in the Hamiltonian into a group of at most 8 commuting operators that act on the qubits in question.”
BackgroundMethodQuantum optimization using variational algorithms on near-term quantum devices
Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., et al. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab822 · Cited by 744
Progress towards practical quantum variational algorithms
Wecker, D., Hastings, M. B., & Troyer, M. (2015) · Physical Review A · doi:10.1103/physreva.92.042303 · Cited by 716
Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz
Romero, J., Babbush, R., McClean, J. R., Hempel, C., Love, P. J., & Aspuru-Guzik, A. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/aad3e4 · Cited by 726
Self-citationQuantum Chemistry Calculations on a Trapped-Ion Quantum Simulator
Hempel, C., Maier, C., Romero, J., McClean, J., Monz, T., Shen, H., Jurcevic, P., Lanyon, B. P., Love, P., Babbush, R., et al. (2018) · Physical Review X · doi:10.1103/physrevx.8.031022 · Cited by 629
“[36] James D Whitfield, Jacob Biamonte, and Alán AspuruGuzik, “Simulation of electronic structure Hamiltonians using quantum computers,” Mol.”
BackgroundSelf-citationThe Bravyi-Kitaev transformation for quantum computation of electronic structure
Seeley, J. T., Richard, M. J., & Love, P. J. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4768229 · Cited by 585
OpenFermion: the electronic structure package for quantum computers
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-citationTang, H. L., Shkolnikov, V. O., Barron, G. S., Grimsley, H. R., Mayhall, N. J., Barnes, E., & Economou, S. E. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020310 · Cited by 408
Superconducting quantum computing: a review
Huang, H.-L., Wu, D., Fan, D., & Zhu, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2881-9 · Cited by 412
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
“[37] Gaussians Trotterization O ( N(4) log (1/ ) ) O (poly (N/ )) O (poly (N/ )) 2013 Wecker et al.”
Potential of quantum computing for drug discovery
Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357
Self-citationExperimental realization of Shor's quantum factoring algorithm using qubit recycling
Martín-López, E., Laing, A., Lawson, T., Alvarez, R., Zhou, X.-Q., & O'Brien, J. L. (2012) · Nature Photonics · doi:10.1038/nphoton.2012.259 · Cited by 349
Low-Depth Quantum Simulation of Materials
Babbush, R., Wiebe, N., McClean, J., McClain, J., Neven, H., & Chan, G. K.-L. (2018) · Physical Review X · doi:10.1103/physrevx.8.011044 · Cited by 335
“[16] JW Gaussians Trotter ΘðN5Þ O(polyðNÞ) O(polyðNÞ)”
BackgroundInfluentialGreen, A. S., Lumsdaine, P. L., Ross, N. J., Selinger, P., & Valiron, B. (2013) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2491956.2462177 · Cited by 378
Strawberry Fields: A Software Platform for Photonic Quantum Computing
Killoran, N., Izaac, J., Quesada, N., Bergholm, V., Amy, M., & Weedbrook, C. (2019) · Quantum · doi:10.22331/q-2019-03-11-129 · Cited by 395
“For example, the ground state energy of large systems, the dynamical behaviour of an ensemble of molecules, or complex molecular behaviour such as protein folding, are often computationally hard or downright impossible to determine via classical computation or experimentation [87, 88].”
BackgroundEven More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction
Lee, J., Berry, D. W., Gidney, C., Huggins, W. J., McClean, J. R., Wiebe, N., & Babbush, R. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.030305 · Cited by 304
Gate-count estimates for performing quantum chemistry on small quantum computers
Wecker, D., Bauer, B., Clark, B. K., Hastings, M. B., & Troyer, M. (2014) · Physical Review A · doi:10.1103/physreva.90.022305 · Cited by 296
Qubit Coupled Cluster Method: A Systematic Approach to Quantum Chemistry on a Quantum Computer
Ryabinkin, I. G., Yen, T.-C., Genin, S. N., & Izmaylov, A. F. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00932 · Cited by 287
Solving strongly correlated electron models on a quantum computer
Wecker, D., Hastings, M. B., Wiebe, N., Clark, B. K., Nayak, C., & Troyer, M. (2015) · Physical Review A · doi:10.1103/physreva.92.062318 · Cited by 285
High-order quantum algorithm for solving linear differential equations
Berry, D. W. (2014) · Journal of Physics A: Mathematical and Theoretical · doi:10.1088/1751-8113/47/10/105301 · Cited by 277
Low-cost error mitigation by symmetry verification
Bonet-Monroig, X., Sagastizabal, R., Singh, M., & O'Brien, T. E. (2018) · Physical Review A · doi:10.1103/physreva.98.062339 · Cited by 250
Error-Mitigated Digital Quantum Simulation
McArdle, S., Yuan, X., & Benjamin, S. C. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.180501 · Cited by 253
Hybrid Quantum-Classical Approach to Correlated Materials
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
“While it has been known for a long time [8, 41] that many quantum problems can be simulated on quantum computers with polynomial scaling, such scaling in the asymptotic regime is insufficient to make an algorithm practical, especially if the power of the polynomial is high and constants are large – as is the case for…”
BackgroundProgramming languages and compiler design for realistic quantum hardware
Chong, F. T., Franklin, D. M., & Martonosi, M. (2017) · Nature · doi:10.1038/nature23459 · Cited by 252
Exact parameterization of fermionic wave functions via unitary coupled cluster theory
Evangelista, F. A., Chan, G. K.-L., & Scuseria, G. E. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5133059 · Cited by 225
A quantum computing view on unitary coupled cluster theory
Anand, A., Schleich, P., Alperin-Lea, S., Jensen, P. W. K., Sim, S., Díaz‐Tinoco, M., Kottmann, J. S., Degroote, M., Izmaylov, A. F., & Aspuru‐Guzik, A. (2022) · Chemical Society Reviews · doi:10.1039/d1cs00932j · Cited by 251
Self-citationFrom transistor to trapped-ion computers for quantum chemistry
Yung, M.-H., Casanova, J., Mezzacapo, A., McClean, J., Lamata, L., Aspuru-Guzik, A., & Solano, E. (2014) · Scientific Reports · doi:10.1038/srep03589 · Cited by 221
“[47]) for implementing each e−iHlt/n involves as many as 2N two-qubit gates for simulating N fermionic modes; in our protocol one needs only two Mølmer-Sørensen gates, which are straightforwardly implementable with current trapped-ion technology.”
MethodSelf-citationQubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization
Berry, D. W., Gidney, C., Motta, M., McClean, J. R., & Babbush, R. (2019) · Quantum · doi:10.22331/q-2019-12-02-208 · Cited by 222
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232
“Explicit, detailed constructions of the circuit representing these terms have been given by Whitfield et al. [165].”
BackgroundChemical basis of Trotter-Suzuki errors in quantum chemistry simulation
Babbush, R., McClean, J., Wecker, D., Aspuru-Guzik, A., & Wiebe, N. (2015) · Physical Review A · doi:10.1103/physreva.91.022311 · Cited by 199
Self-citationAdiabatic Quantum Simulation of Quantum Chemistry
Babbush, R., Love, P. J., & Aspuru-Guzik, A. (2014) · Scientific Reports · doi:10.1038/srep06603 · Cited by 191
“[70] J.”
BackgroundMethodSelf-citationQASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation
Li, A., Stein, S., Krishnamoorthy, S., & Ang, J. (2023) · ACM Transactions on Quantum Computing · doi:10.1145/3550488 · Cited by 183
“We also include two Unitary Coupled Cluster Single-Double VQE ansatz [132] for estimating the ground energy of molecules due to their ultra deep circuits.”
MethodQuantum phase estimation of multiple eigenvalues for small-scale (noisy) experiments
O’Brien, T. E., Tarasinski, B., & Terhal, B. M. (2019) · New Journal of Physics · doi:10.1088/1367-2630/aafb8e · Cited by 175
Emerging quantum computing algorithms for quantum chemistry
Motta, M., & Rice, J. E. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1580 · Cited by 201
“Techniques of circuit recompilation aimed at generating optimal circuit are also intensely investigated [132, 205–207].”
BackgroundTakeshita, T. Y., Rubin, N. C., Zhang, J., Lee, E., Babbush, R., & McClean, J. R. (2020) · Physical Review X · doi:10.1103/physrevx.10.011004 · Cited by 177
“Starting from the original proposal to use quantum phase estimation for chemical problems [2], the precise costs of these algorithms and methods to reduce these costs by orders of magnitude have now been developed in detail [3 – 15].”
MethodKivlichan, I. D., Gidney, C., Berry, D. W., Wiebe, N., McClean, J., Sun, W., Jiang, Z., Rubin, N., Fowler, A., Aspuru-Guzik, A., et al. (2020) · Quantum · doi:10.22331/q-2020-07-16-296 · Cited by 173
“The first quantum circuits for these simulations [8] had O ( N 10 ) scaling where N is the number of single-particle basis functions [9].”
BackgroundSelf-citationTomography and generative training with quantum Boltzmann machines
Kieferová, M., & Wiebe, N. (2017) · Physical Review A · doi:10.1103/physreva.96.062327 · Cited by 170
Nakazawa, S., Nishida, S., Ise, T., Yoshino, T., Mori, N., Rahimi, R. D., Sato, K., Morita, Y., Toyota, K., Shiomi, D., et al. (2012) · Angewandte Chemie International Edition · doi:10.1002/anie.201204489 · Cited by 164
Statistical assertions for validating patterns and finding bugs in quantum programs
Huang, Y., & Martonosi, M. (2019) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3307650.3322213 · Cited by 169
“The procedure for doing this was laid out in detail by Whitfield [54].”
MethodQuantum Algorithms to Simulate Many-Body Physics of Correlated Fermions
Jiang, Z., Sung, K. J., Kechedzhi, K., Smelyanskiy, V. N., & Boixo, S. (2018) · Physical Review Applied · doi:10.1103/physrevapplied.9.044036 · Cited by 175
“(C10) commute with each other, and the first part can be implemented with only single-qubit gates with the JWT. 20”
MethodInfluentialGreen, A. S., Lumsdaine, P. L., Ross, N. J., Selinger, P., & Valiron, B. (2013) · ACM SIGPLAN Notices · doi:10.1145/2499370.2462177 · Cited by 191
“ame of Hex. • Class Number (CL). To approximate the class group of a real quadratic number field [8]. • Ground State Estimation (GSE). To compute the ground state energy level of a particular molecule [23]. • Quantum Linear Systems (QLS). To solve a linear system of equations [9]. • Unique Shortest Vector (USV). To cho…”
BackgroundMethodInfluentialApplication of fermionic marginal constraints to hybrid quantum algorithms
Rubin, N. C., Babbush, R., & McClean, J. (2018) · New Journal of Physics · doi:10.1088/1367-2630/aab919 · Cited by 152
A quantum–quantum Metropolis algorithm
Yung, M., & Aspuru‐Guzik, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111758109 · Cited by 168
Self-citationAjagekar, A., & You, F. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112493 · Cited by 164
Faster quantum chemistry simulation on fault-tolerant quantum computers
Cody Jones, N., Whitfield, J. D., McMahon, P. L., Yung, M.-H., Meter, R. V., Aspuru-Guzik, A., & Yamamoto, Y. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/11/115023 · Cited by 145
“0567v1 [ qu a n t - ph ] 3 A p r 2012 problems such as calculating the eigenvalues of a molecular Hamiltonian [4–7].”
BackgroundMethodInfluentialSelf-citationScaffCC: Scalable compilation and analysis of quantum programs
Javadi-Abhari, A., Patil, S. R., Kudrow, D., Heckey, J., Lvov, A., Chong, F. T., & Martonosi, M. (2014) · Parallel Computing · doi:10.1016/j.parco.2014.12.001 · Cited by 142
Improved techniques for preparing eigenstates of fermionic Hamiltonians
Berry, D. W., Kieferová, M., Scherer, A., Sanders, Y. R., Low, G. H., Wiebe, N., Gidney, C., & Babbush, R. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0071-5 · Cited by 161
Exponentially more precise quantum simulation of fermions in second quantization
Babbush, R., Berry, D. W., Kivlichan, I. D., Wei, A. Y., Love, P. J., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/3/033032 · Cited by 135
Self-citationQubit-excitation-based adaptive variational quantum eigensolver
(2021) · doi:10.1038/s42005-021-00730-0 · Cited by 145
Improving quantum algorithms for quantum chemistry
Hastings, M., Wecker, D., Bauer, B., & Troyer, M. (2014) · Quantum information & computation · doi:10.26421/qic15.1-2-1 · Cited by 208
“Station Q, Microsoft Research, Santa Barbara, CA 93106-6105, USA”
BackgroundJohnson, T. H., Clark, S. R., & Jaksch, D. (2014) · EPJ Quantum Technology · doi:10.1140/epjqt10 · Cited by 127
Exploiting Locality in Quantum Computation for Quantum Chemistry
McClean, J. R., Babbush, R., Love, P. J., & Aspuru-Guzik, A. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz501649m · Cited by 125
Self-citationElectron-Phonon Systems on a Universal Quantum Computer
Macridin, A., Spentzouris, P., Amundson, J. F., & Harnik, R. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.110504 · Cited by 120
Tranter, A., Love, P. J., Mintert, F., & Coveney, P. V. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00450 · Cited by 138
Quantum Computing and Simulations for Energy Applications: Review and Perspective
Paudel, H. P., Syamlal, M., Crawford, S. E., Lee, Y., Shugayev, R., Lu, P., Ohodnicki, P. R., Mollot, D., & Duan, Y. (2022) · ACS Engineering Au · doi:10.1021/acsengineeringau.1c00033 · Cited by 153
Fault-tolerant quantum simulations of chemistry in first quantization
Su, Y., Berry, D. W., Wiebe, N., Rubin, N., & Babbush, R. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.040332 · Cited by 123
Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer
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
Self-citationMeasurement reduction in variational quantum algorithms
Zhao, A., Tranter, A., Kirby, W. M., Ung, S. F., Miyake, A., & Love, P. J. (2020) · Physical Review A · doi:10.1103/physreva.101.062322 · Cited by 121
The Dawn of Quantum Natural Language Processing
Sipio, R. D., Huang, J.-H., Chen, S. Y.-C., Mangini, S., & Worring, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747675 · Cited by 124
(2014) · doi:10.1145/2597917.2597939 · Cited by 152
“The benchmark is parameterized here by the molecular weight (M ), but could also be parameterized by precision [24].”
BackgroundBravyi-Kitaev Superfast simulation of electronic structure on a quantum computer
Setia, K., & Whitfield, J. D. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019371 · Cited by 118
Self-citationTime-dependent Hamiltonian simulation with L 1 -norm scaling
Berry, D. W., Childs, A. M., Su, Y., Wang, X., & Wiebe, N. (2020) · Quantum · doi:10.22331/q-2020-04-20-254 · Cited by 133
“Quantum computers can simulate various physical systems, including condensed matter physics [3], quantum field theory [28], and quantum chemistry [2, 14, 37, 48].”
BackgroundAutomated distribution of quantum circuits via hypergraph partitioning
Andrés-Martínez, P., & Heunen, C. (2019) · Physical Review A · doi:10.1103/physreva.100.032308 · Cited by 112
Efficient quantum circuits for quantum computational chemistry
Yordanov, Y. S., Arvidsson-Shukur, D. R. M., & Barnes, C. H. W. (2020) · Physical Review A · doi:10.1103/physreva.102.062612 · Cited by 105
$O(N^3)$ Measurement Cost for Variational Quantum Eigensolver on Molecular Hamiltonians
Gokhale, P., Angiuli, O., Ding, Y., Gui, K., Tomesh, T., Suchara, M., Martonosi, M., & Chong, F. T. (2020) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2020.3035814 · Cited by 104
Quantum simulation of chemistry with sublinear scaling in basis size
Babbush, R., Berry, D. W., McClean, J. R., & Neven, H. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0199-y · Cited by 121
Fermion-to-qubit mappings with varying resource requirements for quantum simulation
Steudtner, M., & Wehner, S. (2018) · New Journal of Physics · doi:10.1088/1367-2630/aac54f · Cited by 113
Calculating energy derivatives for quantum chemistry on a quantum computer
O’Brien, T. E., Senjean, B., Sagastizabal, R., Bonet-Monroig, X., Dutkiewicz, A., Buda, F., DiCarlo, L., & Visscher, L. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0213-4 · Cited by 108
Electronic Structure Calculations and the Ising Hamiltonian
Xia, R., Bian, T., & Kais, S. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b10371 · Cited by 103
Operator locality in the quantum simulation of fermionic models
Havlíček, V., Troyer, M., & Whitfield, J. D. (2017) · Physical Review A · doi:10.1103/physreva.95.032332 · Cited by 92
“However, systems where the electronelectron interaction cannot be integrated out are called strongly correlated and represent a new frontier for electronic structure in both theoretical chemistry [4] and strongly correlated materials, such as high-temperature superconductors [5].”
BackgroundSelf-citationA Full Quantum Eigensolver for Quantum Chemistry Simulations
Wei, S., Li, H., & Long, G. (2020) · Research · doi:10.34133/2020/1486935 · Cited by 97
“[21] James D Whitfield, Jacob Biamonte, and Alán Aspuru-Guzik.”
BackgroundQubit-excitation-based adaptive variational quantum eigensolver
Yordanov, Y. S., Armaos, V., Barnes, C. H., & Arvidsson-Shukur, D. R. M. (2021) · Apollo (University of Cambridge) · doi:10.17863/cam.75907 · Cited by 95
Quantum computing for neutrino-nucleus scattering
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
High-fidelity quantum state evolution in imperfect photonic integrated circuits
Mower, J., Harris, N. C., Steinbrecher, G. R., Lahini, Y., & Englund, D. R. (2015) · Physical Review A · doi:10.1103/physreva.92.032322 · Cited by 99
Digital quantum computation of fermion-boson interacting systems
Macridin, A., Spentzouris, P., Amundson, J. F., & Harnik, R. (2018) · Physical Review A · doi:10.1103/physreva.98.042312 · Cited by 94
Sugisaki, K., Nakazawa, S., Toyota, K., Sato, K., Shiomi, D., & Takui, T. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00788 · Cited by 89
Adiabatic state preparation study of methylene
Veis, L., & Pittner, J. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4880755 · Cited by 90
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
“We also show the circuit compiled to one-and two-qubit (CNOT) gates. b shows a decomposition of the multi-qubit gates based on a conventional approach to decompose exponentials of Pauli strings into circuits of CNOTs described in [62].”
MethodBabbush, 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
Self-citationError Mitigation via Verified Phase Estimation
O’Brien, T. E., Polla, S., Rubin, N. C., Huggins, W. J., McArdle, S., Boixo, S., McClean, J. R., & Babbush, R. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020317 · Cited by 92
“H s depending on the type of H s chosen (bottom right).”
BackgroundYalouz, S., Senjean, B., Günther, J., Buda, F., O’Brien, T. E., & Visscher, L. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abd334 · Cited by 88
Fermionic quantum processing with programmable neutral atom arrays
González-Cuadra, D., Bluvstein, D., Kalinowski, M., Kaubruegger, R., Maskara, N., Naldesi, P., Zache, T. V., Kaufman, A. M., Lukin, M. D., Pichler, H., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304294120 · Cited by 104
“· U (t) i,k j,l ( √ 2 , 2 2− 4 , ), [9]”
Simulating quantum materials with digital quantum computers
Oftelie, L. B., Urbánek, M., Metcalf, M., Carter, J. T., Kemper, A. F., & de Jong, W. A. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac1ca6 · Cited by 83
Metcalf, M., Bauman, N. P., Kowalski, K., & de Jong, W. A. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00421 · Cited by 79
An Introduction to Quantum Programming in Quipper
Green, A. S., Lumsdaine, P., Ross, N. J., Selinger, P., & Valiron, B. (2013) · International Workshop on Reversible Computation · doi:10.1007/978-3-642-38986-3_10 · Cited by 87
“Quipper’s development was guided by the goal of implementing seven non-trivial quantum algorithms from the literature [3,5,11,12,14,17,18].”
MethodConti, I., Cerullo, G., Nenov, A., & Garavelli, M. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c04952 · Cited by 76
Local spin operators for fermion simulations
Whitfield, J. D., Havlíček, V., & Troyer, M. (2016) · Physical Review A · doi:10.1103/physreva.94.030301 · Cited by 70
Self-citationQuantum-circuit design for efficient simulations of many-body quantum dynamics
Raeisi, S., Wiebe, N., & Sanders, B. C. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/10/103017 · Cited by 72
The Trotter step size required for accurate quantum simulation of quantum chemistry
Poulin, D., Hastings, M. B., Wecker, D., Wiebe, N., Doberty, A. C., & Troyer, M. (2015) · Quantum Information and Computation · doi:10.26421/qic15.5-6-1 · Cited by 159
“We note that this simulation of free Hamiltonians is exact, in contrast to previous approaches [11, 12, 13, 1, 2] that rely on TS approximations.”
MethodInfluential
Metadata
- Authors
- Whitfield, J. D., Biamonte, J., & Aspuru-Guzik, A.
- Affiliations
- Harvard University (US)
- Corresponding author
- Whitfield, James D.
- Biamonte, Jacob
- ORCID
- Biamonte, Jacob
- Venue
- Molecular Physics
- Publisher
- Informa UK Limited
- Published
- 2011-03-10
- Volume
- 109
- Issue
- 5
- Pages
- 735-750
- ISSN
- 0026-8976 (ISSN-L)
- 1362-3028
- OpenAlex
- W2103373613
- Cited by
- 472
- References
- 36
- Citations by year
- 2026: 33 · 2025: 54 · 2024: 54 · 2023: 64 · 2022: 49 · 2021: 36 · 2020: 43 · 2019: 23 · 2018: 29 · 2017: 13 · 2016: 14 · 2015: 14 · 2014: 19 · 2013: 11 · 2012: 12
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Molecular Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/G003017/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum phase estimation, quantum circuits, molecular integrals, adiabatic state preparation, molecular hydrogen, quantum chemistry, quantum computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
