Prospects of quantum computing for molecular sciences
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Abstract Molecular science is governed by the dynamics of electrons and atomic nuclei, and by their interactions with electromagnetic fields. A faithful physicochemical understanding of these processes is crucial for the design and synthesis of chemicals and materials of value for our society and economy. Although some problems in this field can be adequately addressed by classical mechanics, many demand an explicit quantum mechanical description. Such quantum problems require a representation of wave functions that grows exponentially with system size and therefore should naturally benefit from quantum computation on a number of logical qubits that scales only linearly with system size. In this perspective, we elaborate on the potential benefits of quantum computing in the molecular sciences, i.e., in molecular physics, chemistry, biochemistry, and materials science.
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Quantum computers have been proposed to simulate quantum systems efficiently.
“By exploiting their quantum mechanical properties, quantum computers have been proposed to simulate quantum systems efficiently [9–13].”
From Drug design on quantum computers (2024) · cites this paper as [12]
Quantum phase estimation algorithms are likely to compete with and beat the best traditional approaches for strong electron correlation cases, such as metalloenzyme active sites and electronically excited states.
“Detailed resource estimates [13,140] showed that algorithms based on quantum phase estimation are likely to compete and beat the best traditional approaches for cases of strong electron correlation as they occur in active sites of metalloenzymes (and in electronically excited states; see below).”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [13]
As the molecule size increases, VQE requires a larger number of sub cycles for Hamiltonian averaging, projecting to unfeasibly long runtime for systems of practical importance.
“as the molecule size increases, VQE requires a larger number of sub cycles for Hamiltonian averaging (because of more terms in the Hamiltonian), projecting to unfeasibly long runtime for systems of practical importance [90, 102, 106, 107]”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [106]
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Cites
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135 · see citing works
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541 · see citing works
Density-Functional Theory for Time-Dependent Systems
Runge, E., & Gross, E. K. U. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.997 · Cited by 8,914 · see citing works
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · see citing works
A fifth-order perturbation comparison of electron correlation theories
Raghavachari, K., Trucks, G. W., Pople, J. A., & Head‐Gordon, M. (1989) · Chemical Physics Letters · doi:10.1016/s0009-2614(89)87395-6 · Cited by 8,511 · see citing works
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436 · see citing works
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720 · see citing works
Density matrix formulation for quantum renormalization groups
White, S. R. (1992) · Physical Review Letters · doi:10.1103/physrevlett.69.2863 · Cited by 7,702 · see citing works
A full coupled-cluster singles and doubles model: The inclusion of disconnected triples
Purvis, G. D., & Bartlett, R. J. (1982) · The Journal of Chemical Physics · doi:10.1063/1.443164 · Cited by 6,538 · see citing works
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091 · see citing works
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works
Quantum Mechanics Helps in Searching for a Needle in a Haystack
Grover, L. K. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.325 · Cited by 4,306 · cited 1× · see citing works
“Grover search (Grover 1997) can provide a quadratic speedup compared to classical brute force search on the potential energy surface (PES).”
BackgroundA complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach
Roos, B. O., Taylor, P. R., & Sigbahn, P. E. M. (1980) · Chemical Physics · doi:10.1016/0301-0104(80)80045-0 · Cited by 3,982 · see citing works
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1999) · SIAM Review · doi:10.1137/s0036144598347011 · Cited by 3,904 · see citing works
Density Functional Theory of Electronic Structure
Kohn, W., Becke, A. D., & Parr, R. G. (1996) · The Journal of Physical Chemistry · doi:10.1021/jp960669l · Cited by 3,464 · see citing works
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343 · see citing works
Second-order perturbation theory with a CASSCF reference function
Andersson, K., Malmqvist, P. A., Roos, B. O., Sadlej, A. J., & Woliński, K. (1990) · The Journal of Physical Chemistry · doi:10.1021/j100377a012 · Cited by 3,334 · see citing works
Quantum Algorithm for Linear Systems of Equations
Harrow, A. W., Hassidim, A., & Lloyd, S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.150502 · Cited by 3,325 · see citing works
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · see citing works
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M. (2017) · Nature · doi:10.1038/nature23879 · Cited by 2,982 · see citing works
Configuration interaction calculations on the nitrogen molecule
Langhoff, S. R., & Davidson, E. R. (1974) · International Journal of Quantum Chemistry · doi:10.1002/qua.560080106 · Cited by 2,910 · see citing works
QM/MM Methods for Biomolecular Systems
Senn, H. M., & Thiel, W. (2009) · Angewandte Chemie International Edition · doi:10.1002/anie.200802019 · Cited by 2,692 · see citing works
Stanton, J. F., & Bartlett, R. J. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464746 · Cited by 2,603 · see citing works
Davidson, E. R. (1975) · Journal of Computational Physics · doi:10.1016/0021-9991(75)90065-0 · Cited by 2,559 · see citing works
Beck, M. (2000) · Physics Reports · doi:10.1016/s0370-1573(99)00047-2 · Cited by 2,463 · see citing works
Quantum computational advantage using photons
Zhong, H.-S., Wang, H., Deng, Y.-H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Qin, J., Wu, D., Ding, X., Hu, Y., et al. (2020) · Science · doi:10.1126/science.abe8770 · Cited by 2,405 · see citing works
Barren plateaus in quantum neural network training landscapes
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2018) · Nature Communications · doi:10.1038/s41467-018-07090-4 · Cited by 2,329 · see citing works
Quantum Monte Carlo simulations of solids
Foulkes, W. M. C., Mitáš, L., Needs, R. J., & Rajagopal, G. (2001) · Reviews of Modern Physics · doi:10.1103/revmodphys.73.33 · Cited by 2,318 · see citing works
The 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 · see citing works
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215 · see citing works
Self-citationThe multi-configurational time-dependent Hartree approach
Meyer, H., Manthe, U., & Cederbaum, L. S. (1990) · Chemical Physics Letters · doi:10.1016/0009-2614(90)87014-i · Cited by 2,025 · see citing works
Introduction of n-electron valence states for multireference perturbation theory
Angeli, C., Cimiraglia, R., Evangelisti, S., Leininger, T., & Malrieu, J. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1361246 · Cited by 1,964 · see citing works
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
Anharmonic vibrational properties by a fully automated second-order perturbative approach
Barone, V. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1824881 · Cited by 1,666 · see citing works
Quantum 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 · see citing works
Simulated Quantum Computation of Molecular Energies
Aspuru-Guzik, A., Dutoi, A. D., Love, P. J., & Head-Gordon, M. (2005) · Science · doi:10.1126/science.1113479 · Cited by 1,418 · see citing works
Laurent, A. D., & Jacquemin, D. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24438 · Cited by 1,385 · see citing works
Quantum 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 · see citing works
Molecular Geometry Optimization with a Genetic Algorithm
Deaven, D. M., & Ho, K. M. (1995) · Physical Review Letters · doi:10.1103/physrevlett.75.288 · Cited by 1,149 · see citing works
OpenMolcas: From Source Code to Insight
Galván, I. F., Vacher, M., Alavi, A., Angeli, C., Aquilante, F., Autschbach, J., Bao, J. J., Bokarev, S. I., Bogdanov, N. A., Carlson, R. K., et al. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00532 · Cited by 1,115 · see citing works
Self-citationBeyond the random-phase approximation: A new approximation scheme for the polarization propagator
Schirmer, J. (1982) · Physical Review A · doi:10.1103/physreva.26.2395 · Cited by 1,103 · see citing works
Quantum algorithms: an overview
Montanaro, A. (2016) · npj Quantum Information · doi:10.1038/npjqi.2015.23 · Cited by 1,071 · cited 2× · see citing works
“Quantum computing promises exponential speedups over traditional computing for certain computational problems (Shor 1999; Abrams and Lloyd 1999; Harrow et al. 2009; Childs et al. 2003; Kassal et al. 2008; Montanaro 2016; Cao et al. 2019; Emani et al. 2021; Outeiral et al. 2021).”
BackgroundEnergy extrapolation in CI calculations
Buenker, R. J., & Peyerimhoff, S. D. (1975) · Theoretical Chemistry Accounts · doi:10.1007/bf00555301 · Cited by 1,009 · see citing works
Free energy from constrained molecular dynamics
Sprik, M., & Ciccotti, G. (1998) · The Journal of Chemical Physics · doi:10.1063/1.477419 · Cited by 944 · see citing works
Solution of systems of linear equations by minimized iterations
Lanczos, C. (1952) · Journal of research of the National Bureau of Standards · doi:10.6028/jres.049.006 · Cited by 935 · see citing works
Individualized configuration selection in CI calculations with subsequent energy extrapolation
Buenker, R. J., & Peyerimhoff, S. D. (1974) · Theoretica Chimica Acta · doi:10.1007/pl00020553 · Cited by 880 · see citing works
Hamiltonian Simulation by Qubitization
Low, G. H., & Chuang, I. L. (2019) · Quantum · doi:10.22331/q-2019-07-12-163 · Cited by 854 · see citing works
Self-citationBooth, G. H., Thom, A. J. W., & Alavi, A. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3193710 · Cited by 853 · see citing works
Quantum 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 · see citing works
Ab Initio Multiple Spawning: Photochemistry from First Principles Quantum Molecular Dynamics
Ben-Nun, M., Quenneville, J., & Martı́nez, T. J. (2000) · The Journal of Physical Chemistry A · doi:10.1021/jp994174i · Cited by 835 · see citing works
Exponential algorithmic speedup by a quantum walk
Childs, A. M., Cleve, R., Deotto, E., Farhi, E., Gutmann, S., & Spielman, D. A. (2003) · Proceedings of the thirty-fifth annual ACM symposium on Theory of computing · doi:10.1145/780542.780552 · Cited by 825 · see citing works
Hartree-Fock on a superconducting qubit quantum computer
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 824 · see citing works
Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors
Abrams, D. S., & Lloyd, S. (1999) · Physical Review Letters · doi:10.1103/physrevlett.83.5162 · Cited by 817 · see citing works
Elucidating 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 · see citing works
Self-citationProgress 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 · cited 1× · see citing works
“Furthermore, the 1011 measurements per optimization step (Wecker et al. 2015) further increase the cost.”
BackgroundSelf-citationHamiltonian Simulation by Qubitization
Low, G. H., & Chuang, I. L. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 714 · see citing works
Self-citationSzalay, P. G., Müller, T., Gidofalvi, G., Lischka, H., & Shepard, R. L. (2011) · Chemical Reviews · doi:10.1021/cr200137a · Cited by 713 · see citing works
Using metadynamics to explore complex free-energy landscapes
Bussi, G., & Laio, A. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0153-0 · Cited by 613 · see citing works
Self-consistent field energies and wavefunctions for coupled oscillators
Bowman, J. M. (1978) · The Journal of Chemical Physics · doi:10.1063/1.435782 · Cited by 611 · see citing works
(2004) · doi:10.1109/focs.2004.53 · Cited by 599 · see citing works
Response functions in the CC3 iterative triple excitation model
Christiansen, O., Koch, H., & rgensen, P. J. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470315 · Cited by 588 · see citing works
Deep-neural-network solution of the electronic Schrödinger equation
Hermann, J., Schätzle, Z., & Noé, F. (2020) · Nature Chemistry · doi:10.1038/s41557-020-0544-y · Cited by 559 · see citing works
<i>Ab initio</i> quantum chemistry using the density matrix renormalization group
White, S. R., & Martin, R. L. (1999) · The Journal of Chemical Physics · doi:10.1063/1.478295 · Cited by 558 · see citing works
Demonstration of the trapped-ion quantum CCD computer architecture
Pino, J. M., Dreiling, J. M., Figgatt, C., Gaebler, J. P., Moses, S. A., Allman, M. S., Baldwin, C. H., Foss-Feig, M., Hayes, D., Mayer, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03318-4 · Cited by 547 · see citing works
Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states
McClean, J. R., Kimchi-Schwartz, M. E., Carter, J., & de Jong, W. A. (2017) · Physical Review A · doi:10.1103/physreva.95.042308 · Cited by 516 · see citing works
Are atoms intrinsic to molecular electronic wavefunctions? I. The FORS model
Ruedenberg, K., Schmidt, M. W., Gilbert, M., & Elbert, S. T. (1982) · Chemical Physics · doi:10.1016/0301-0104(82)87004-3 · Cited by 511 · see citing works
Variational Quantum Computation of Excited States
Higgott, O., Wang, D., & Brierley, S. (2019) · Quantum · doi:10.22331/q-2019-07-01-156 · Cited by 463 · see citing works
Understanding the Surface Hopping View of Electronic Transitions and Decoherence
Subotnik, J. E., Jain, A., Landry, B. R., Petit, A. S., Ouyang, W., & Bellonzi, N. (2016) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-040215-112245 · Cited by 440 · see citing works
Manthe, U. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2902982 · Cited by 427 · see citing works
Training Variational Quantum Algorithms Is NP-Hard
Bittel, L., & Kliesch, M. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.120502 · Cited by 398 · see citing works
Polynomial-time quantum algorithm for the simulation of chemical dynamics
Kassal, I., Jordan, S. P., Love, P. J., Mohseni, M., & Aspuru-Guzik, A. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808245105 · Cited by 385 · see citing works
Schlegel, H. B. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.34 · Cited by 365 · see citing works
Ground-state energy estimation of the water molecule on a trapped-ion quantum computer
Nam, Y., Chen, J.-S., Pisenti, N. C., Wright, K., Delaney, C. P., Maslov, D., Brown, K. R., Allen, S., Amini, J., Apisdorf, J., et al. (2020) · npj Quantum Information · doi:10.1038/s41534-020-0259-3 · Cited by 346 · see citing works
Ghosh, D., Hachmann, J., Yanai, T., & Chan, G. K. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2883976 · Cited by 333 · see citing works
Vibrational coupled cluster theory
Christiansen, O. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1637579 · Cited by 333 · see citing works
Kurashige, Y., & Yanai, T. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3629454 · Cited by 312 · see citing works
Baiardi, A., & Reiher, M. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5129672 · Cited by 306 · see citing works
Self-citationApplication of SCF-SI theory to vibrational motion in polyatomic molecules
Bowman, J. M., Christoffel, K. M., & Tobin, F. L. (1979) · The Journal of Physical Chemistry · doi:10.1021/j100471a005 · Cited by 305 · see citing works
Even 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 · see citing works
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 · see citing works
Piotr Piecuch, B., Kowalski, K., Pimienta, I. S. O., & McGuire, M. J. (2002) · International Reviews in Physical Chemistry · doi:10.1080/0144235021000053811 · Cited by 283 · see citing works
Quantum computing enhanced computational catalysis
von Burg, V., Low, G. H., Häner, T., Steiger, D. S., Reiher, M., Roetteler, M., & Troyer, M. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033055 · Cited by 259 · see citing works
Self-citationThe 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 · see citing works
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 · see citing works
Machine learning accurate exchange and correlation functionals of the electronic density
Dick, S., & Fernández-Serra, M. (2020) · Nature Communications · doi:10.1038/s41467-020-17265-7 · Cited by 217 · see citing works
Manni, G. L., Smart, S. D., & Alavi, A. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b01190 · Cited by 204 · see citing works
Mixed quantum-classical equilibrium: Surface hopping
Schmidt, J. R., Parandekar, P. V., & Tully, J. C. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2955564 · Cited by 196 · see citing works
Sinnecker, S., Neese, F., Noodleman, L., & Lubitz, W. (2004) · Journal of the American Chemical Society · doi:10.1021/ja0390202 · Cited by 194 · see citing works
Quantum 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 · see citing works
Noisy intermediate-scale quantum (NISQ) algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2021) · arXiv · doi:10.48550/arxiv.2101.08448 · Cited by 166 · see citing works
Sawaya, N. P. D., Menke, T., Kyaw, T. H., Johri, S., Aspuru-Guzik, A., & Guerreschi, G. G. (2020) · npj Quantum Information · doi:10.1038/s41534-020-0278-0 · Cited by 163 · see citing works
Quantum computing at the frontiers of biological sciences
Emani, P. S., Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., Sapiro, G., Aspuru-Guzik, A., Baker, J. T., Bastiani, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-020-01004-3 · Cited by 163 · see citing works
Hartenfeller, M., & Schneider, G. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_12 · Cited by 160 · see citing works
The Ground State Electronic Energy of Benzene
Eriksen, J. J., Anderson, T. A., Deustua, J. E., Ghanem, K., Hait, D., Hoffmann, M. R., Lee, S., Levine, D. S., Magoulas, I., Shen, J., et al. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02621 · Cited by 149 · see citing works
Exponential algorithmic speedup by a quantum walk
Childs, A. M., Cleve, R., Deotto, E., Farhi, E., Gutmann, S., & Spielman, D. A. (2003) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/780551.780552 · Cited by 135 · see citing works
Molecular Collision Dynamics on Several Electronic States
Martı́nez, T. J., Ben-Nun, M., & Levine, R. D. (1997) · The Journal of Physical Chemistry A · doi:10.1021/jp970842t · Cited by 121 · see citing works
Fast Quantum Algorithm for Numerical Gradient Estimation
Jordan, S. P. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.050501 · Cited by 117 · see citing works
QM/MM free energy simulations: recent progress and challenges
Lu, X., Fang, D., Ito, S., Okamoto, Y., Ovchinnikov, V., & Cui, Q. (2016) · Molecular Simulation · doi:10.1080/08927022.2015.1132317 · Cited by 116 · see citing works
Cheng, L., Welborn, M., Christensen, A. S., & Miller, T. F. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5088393 · Cited by 115 · see citing works
Quantum Chemistry and Dynamics of Excited States
González, L. 1., & Lindh, R. 1. (2020) · Wiley eBooks · doi:10.1002/9781119417774 · Cited by 113 · see citing works
Direct Comparison of Many-Body Methods for Realistic Electronic Hamiltonians
Williams, K. T., Yao, Y., Li, J., Chen, L., Shi, H., Motta, M., Niu, C., Ray, U., Guo, S., Anderson, R., et al. (2020) · Physical Review X · doi:10.1103/physrevx.10.011041 · Cited by 110 · see citing works
Geometry optimization and conformational analysis through generalized simulated annealing
Mundim, K. C., & Tsallis, C. (1998) · International Journal of Quantum Chemistry · doi:10.1002/(sici)1097-461x(1996)58:4 · Cited by 108 · see citing works
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 100 · see citing works
Chemoinformatics: Achievements and Challenges, a Personal View
Gasteiger, J. (2016) · Molecules · doi:10.3390/molecules21020151 · Cited by 91 · see citing works
DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
Chen, Y., Zhang, L., Wang, H., & Weinan, E. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00872 · Cited by 88 · see citing works
Demonstration of the trapped-ion quantum CCD computer architecture
Pino, J. M., Dreiling, J., Figgatt, C., Gaebler, J., Moses, S. A., Allman, M. S., Baldwin, C. H., Foss‐Feig, M., Hayes, D., Mayer, K., et al. (2021) · RePEc: Research Papers in Economics · Cited by 87 · see citing works
Hardware efficient quantum algorithms for vibrational structure calculations
Ollitrault, P. J., Baiardi, A., Reiher, M., & Tavernelli, I. (2020) · Chemical Science · doi:10.1039/d0sc01908a · Cited by 85 · see citing works
Self-citationCross-Comparisons between Experiment, TD-DFT, CC, and ADC for Transition Energies
Suellen, C., Freitas, R. G., Loos, P., & Jacquemin, D. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00446 · Cited by 82 · see citing works
Unbiasing the initiator approximation in full configuration interaction quantum Monte Carlo
Ghanem, K., Lozovoi, A. Y., & Alavi, A. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5134006 · Cited by 78 · see citing works
Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy
Chen, Y., Zhang, L., Wang, H., & Weinan, E. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c03886 · Cited by 75 · see citing works
Accuracy and Resource Estimations for Quantum Chemistry on a Near-Term Quantum Computer
Kühn, M., Zanker, S., Deglmann, P., Marthaler, M., & Weiß, H. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00236 · Cited by 71 · see citing works
Vibrational Density Matrix Renormalization Group
Baiardi, A., Stein, C. J., Barone, V., & Reiher, M. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00329 · Cited by 70 · see citing works
Self-citationQuantum computing applied to calculations of molecular energies: CH2 benchmark
Veis, L., & Pittner, J. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3503767 · Cited by 66 · see citing works
Kawashima, Y., Lloyd, E., Coons, M. P., Nam, Y., Matsuura, S., Garza, A. J., Johri, S., Huntington, L. M. J., Senicourt, V., Maksymov, A., et al. (2021) · Communications Physics · doi:10.1038/s42005-021-00751-9 · Cited by 64 · see citing works
Machine learning meets quantum foundations: A brief survey
Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C. (2020) · AVS Quantum Science · doi:10.1116/5.0007529 · Cited by 59 · see citing works
Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm
Lemieux, J., Heim, B., Poulin, D., Svore, K., & Troyer, M. (2020) · Quantum · doi:10.22331/q-2020-06-29-287 · Cited by 55 · see citing works
Self-citationBauman, N. P., Liu, H., Bylaska, E. J., Krishnamoorthy, S., Low, G. H., Granade, C., Wiebe, N., Baker, N., Peng, B., Roetteler, M., et al. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00909 · Cited by 39 · see citing works
Self-citationMotta, M., Ceperley, D. M., Chan, G. K., Gomez, J. A., Gull, E., Guo, S., Jiménez-Hoyos, C. A., Tran, L. N., Li, J., Ma, F., et al. (2017) · eScholarship (California Digital Library) · doi:10.48550/arxiv.1705.01608 · Cited by 19 · see citing works
Assertion-based optimization of Quantum programs
Häner, T., Hoefler, T., & Troyer, M. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428201 · Cited by 17 · see citing works
Self-citationKawashima, Y., Coons, M. P., Nam, Y., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-209059/v1 · Cited by 7 · see citing works
Kawashima, Y., Lloyd, E., Coons, M. P., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07045 · Cited by 3 · see citing works
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Practical 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 690 · see citing works
“This will open the possibility to simulate processes in quantum chemistry 13,92 and materials science 93,94 , where the effective models are often complex and involve long-range couplings.”
BackgroundSelf-citationDrug design on quantum computers
(2024) · doi:10.1038/s41567-024-02411-5 · Cited by 132 · see citing works
“One of the most commonly cited justifications for the investments is the application of quantum computers to enhance quantum-chemistry calculations 12–14 .”
InfluentialQuantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93 · see citing works
Self-citationQuantum embedding theories to simulate condensed systems on quantum computers
Vorwerk, C., Sheng, N., Govoni, M., Huang, B., & Galli, G. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00279-0 · Cited by 65 · see citing works
Quantum computing and machine learning for Arabic language sentiment classification in social media
Omar, A., & El‐Hafeez, T. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-44113-7 · Cited by 59 · see citing works
“Quantum cryptography: quantum computing can be used to develop quantum cryptography algorithms, which can offer improved security in communication systems 19 8.”
BackgroundQuantum computing algorithms: getting closer to critical problems in computational biology
Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac437 · Cited by 47 · see citing works
“Moreover, as the molecule size increases, VQE requires a larger number of sub cycles for Hamiltonian averaging (because of more terms in the Hamiltonian), projecting to unfeasibly long runtime for systems of practical importance [90, 102, 106, 107].”
BackgroundLocalized Quantum Chemistry on Quantum Computers
Otten, M., Hermes, M. R., Pandharkar, R., Alexeev, Y., Gray, S. K., & Gagliardi, L. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00388 · Cited by 40 · see citing works
A Vision for the Future of Multiscale Modeling
Capone, M., Romanelli, M., Castaldo, D., Parolin, G., Bello, A., Gil, G., & Vanzan, M. (2024) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00080 · Cited by 38 · see citing works
Quantum Computing for Climate Resilience and Sustainability Challenges
Ho, K. T. M., Chen, K.-C., Lee, L., Burt, F., Yu, S., & Lee, P.-H. H. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10289 · Cited by 42 · see citing works
Camino, B., Buckeridge, J., Warburton, P., Kendon, V., & Woodley, S. (2023) · Journal of Applied Physics · doi:10.1063/5.0151346 · Cited by 32 · see citing works
Quantum computation for periodic solids in second quantization
Ivanov, A. V., Sünderhauf, C., Holzmann, N., Ellaby, T., Kerber, R. N., Jones, G. A., & Camps, J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013200 · Cited by 33 · see citing works
“While VQE might have its merits in certain use cases, it appears the emerging consensus is that QPE has a superior scaling with the system size [1, 55].”
BackgroundMolecule‐Specific Uncertainty Quantification in Quantum Chemical Studies
Reiher, M. (2022) · Israel Journal of Chemistry · doi:10.1002/ijch.202100101 · Cited by 29 · see citing works
Self-citationVariational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals
Griol-Barres, I., Milla, S., Cebrián, A., Mansoori, Y., & Millet, J. (2021) · Applied Sciences · doi:10.3390/app11146427 · Cited by 33 · see citing works
“These simulations have a wide range of applications such as the design of new materials or the discovery of new drugs [40].”
BackgroundBlum, V., Asahi, R., Autschbach, J., Bannwarth, C., Bihlmayer, G., Blügel, S., Burns, L. A., Crawford, T. D., Dawson, W., de Jong, W. A., et al. (2024) · Electronic Structure · doi:10.1088/2516-1075/ad48ec · Cited by 24 · see citing works
Self-citationModeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective
Hariharan, S., Kinge, S., & Visscher, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01212 · Cited by 20 · see citing works
Quantum simulations of chemistry in first quantization with any basis set
Georges, T. N., Bothe, M., Sünderhauf, C., Berntson, B. K., Izsák, R., & Ivanov, A. V. (2025) · npj Quantum Information · doi:10.1038/s41534-025-00987-1 · Cited by 21 · see citing works
Fault-Tolerant Quantum Algorithm for Symmetry-Adapted Perturbation Theory
Cortes, C. L., Loipersberger, M., Parrish, R. M., Morley-Short, S., Pol, W., Sim, S., Steudtner, M., Tautermann, C. S., Degroote, M., Moll, N., et al. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.010336 · Cited by 17 · see citing works
Wulff, A., Chen, B., Steinberg, M., Tang, Y., Möller, M., & Feld, S. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117380 · Cited by 16 · see citing works
“For DMRG, we conducted optimizations for maximum bond dimensions of 2, 4, 8, 16, and 32.”
MethodGuaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping
Gresch, A., & Kliesch, M. (2025) · Nature Communications · doi:10.1038/s41467-024-54859-x · Cited by 20 · see citing works
“For chemical use cases where one frequently requires at least chemical accuracy of ϵ chem : acc : (cid:6) 1 : 6 mHa 54 , these methods cannot be expected to deliver suf fi ciently accurate estimations with reasonable measurement effort.”
Utilizing Resource Estimation for the Development of Quantum Computing Applications
Quetschlich, N., Soeken, M., Murali, P., & Wille, R. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00036 · Cited by 16 · see citing works
“Although this program represents a small problem instance that is easily executable on current NISQ computers, VQE generally does not scale well with respect to the size of the problem considered compared to other approaches, e.g., based on Quantum Phase Estimation (QPE) [29].”
High Ground State Overlap via Quantum Embedding Methods
Eraković, M., Witteveen, F., Harley, D., Günther, J., Bensberg, M., Meitei, O. R., Cho, M., Voorhis, T. V., Reiher, M., & Christandl, M. (2025) · PRX Life · doi:10.1103/prxlife.3.013003 · Cited by 14 · see citing works
“We emphasize that rigorous error estimates will be a key advantage over traditional approaches [1,2,22], apart from the fact that a quantum computer with a sufficiently large number of qubits for the representation of an electronic state will tame the curse of dimensionality posed to traditional…”
Self-citationLoipersberger, M., Malone, F. D., Welden, A. R., Parrish, R. M., Fox, T., Degroote, M., Kyoseva, E. S., Moll, N., Santagati, R., & Streif, M. (2023) · Chemical Science · doi:10.1039/d2sc05896k · Cited by 17 · see citing works
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 19 · see citing works
“[ 70, 101]; within today’s NISQ regime we therefore deploy quantum components as physics-aware evaluators for small candidate sets with resource reporting for high-throughput generation, and we note that standardized, hardware-run quantum leaderboards for de novo generation are not yet available…”
Kjellgren, E. R., Reinholdt, P., Fitzpatrick, A., Talarico, N. W., Jensen, P. W. K., Sauer, S. P. A., Coriani, S., Knecht, S., & Kongsted, J. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0190594 · Cited by 11 · see citing works
Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation
Kiss, O., Azad, U., Requena, B., Roggero, A., Wakeham, D., & Arrazola, J. M. (2025) · Quantum · doi:10.22331/q-2025-04-01-1682 · Cited by 11 · see citing works
Faletič, S., Bitzenbauer, P., Bondani, M., Chiofalo, M. L., Goorney, S., Krijtenburg-Lewerissa, K., Mishina, O. S., Müller, R., Pospiech, G., Ercan, İ., et al. (2024) · Challenges in physics education · doi:10.1007/978-3-031-72541-8_15 · Cited by 10 · see citing works
Magnusson, E., Fitzpatrick, A., Knecht, S., Rahm, M., & Dobrautz, W. (2024) · Faraday Discussions · doi:10.1039/d4fd00039k · Cited by 10 · see citing works
“[12–15] Unfortunately, noise severely limits practicable circuit depths on current and near-term quantum processors.”
BackgroundTrenev, D., Ollitrault, P. J., Harwood, S. M., Gujarati, T. P., Raman, S., Mezzacapo, A., & Mostame, S. (2025) · Quantum · doi:10.22331/q-2025-02-11-1630 · Cited by 8 · see citing works
“Quantum computing, on the other hand, o ff ers an alternative approach 1 to tackle the computational challenges associated with these problems using quantum hardware [3, 27, 7].”
Molecular Dynamics Workflow Decomposition for Hybrid Classic/Quantum Systems
Cranganore, S. S., Maio, V. D., Brandić, I., Anh, T. M., & Deelman, E. (2022) · IEEE International Conference on eScience · doi:10.1109/escience55777.2022.00048 · Cited by 8 · see citing works
“Also, in [27] it is discussed how many problems inherent to MD simulations can be more efficiently solved by applying quantum computing methods.”
BackgroundHierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies
Bensberg, M., Eckhoff, M., Husistein, R. T., Teynor, M. S., Sora, V., Bro‐Jørgensen, W., Thomasen, F. E., Krogh, A., Lindorff‐Larsen, K., Solomon, G. C., et al. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00389 · Cited by 7 · see citing works
Self-citationQuantum computing for faster enzyme discovery and engineering
Damborský, J., Kouba, P., Šivic, J., Vasina, M., Bednář, D., & Mazurenko, S. (2025) · Nature Catalysis · doi:10.1038/s41929-025-01410-w · Cited by 9 · see citing works
“Selected examples illustrating the practical application of quantum computing in protein science have been published in the scientific literature (Table 3) and covered by some excellent recent reviews 20–23 .”
Prioritizing quantum computing use cases in the drug discovery and development pipeline
Kovyrshin, A., Tornberg, L., Crain, J., Mensa, S., Tavernelli, I., & Broo, A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104323 · Cited by 6 · see citing works
Qasemnazhand, M., Khoeini, F., Bazrafshan, M. A., & Ayesh, A. I. (2024) · Journal of Physics and Chemistry of Solids · doi:10.1016/j.jpcs.2024.112441 · Cited by 6 · see citing works
Sebastiano, F. (2025) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2025.3581166 · Cited by 6 · see citing works
Mittal, S., & Prusty, B. G. (2026) · Composites Part B Engineering · doi:10.1016/j.compositesb.2026.113658 · Cited by 8 · see citing works
Świerkowska, A., Echavarria, J., Schulz, L. B., & Schulz, M. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10297 · Cited by 6 · see citing works
Quantum Computing in Healthcare - Unlocking New Frontiers in Drug Discovery and Data Management
Raju, N., Quazi, F., & Kondle, P. (2024) · International Journal of Global Innovations and Solutions (IJGIS) · doi:10.21428/e90189c8.e5dab7bb · Cited by 5 · see citing works
Nagy, D., Reinholdt, P., Jensen, P. W. K., Kjellgren, E. R., Ziems, K. M., Fitzpatrick, A., Knecht, S., Kongsted, J., Coriani, S., & Sauer, S. P. A. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c02697 · Cited by 5 · see citing works
“A number of different comprehensive reviews discuss the current state of development of theories, methods, and algorithms that focus on performing quantum mechanical calculations on near-term noisy intermediate-scale quantum (NISQ) devices and on far-future fault-tolerant ones [1–8].”
BackgroundQuantum Computing Based Design of Multivariate Porous Materials
Kang, S., Kim, Y.-H., & Kim, J. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00918 · Cited by 5 · see citing works
“Molecular science, which is governed by the dynamics of electrons and atomic nuclei and their interactions with electromagnetic fields, often requires detailed quantum mechanical models for accurate predictions 36 , but near-term quantum devices currently impose computational limitations.”
Exploring Hybrid Classical-Quantum Compute Systems through Simulation
Farooqi, M. N., & Ruefenacht, M. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10196 · Cited by 5 · see citing works
Massaccesi, G. E., Oña, O. B., Capuzzi, P., Melo, J. I., Laín, L., Torre, A., Peralta, J. E., Alcoba, D. R., & Scuseria, G. E. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01166 · Cited by 5 · see citing works
Flexibility of the factorized form of the unitary coupled cluster Ansatz
Chen, J., Cheng, H., & Freericks, J. K. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0074311 · Cited by 5 · see citing works
Quantum information processing with superconducting circuits: A perspective
Wendin, G. P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90800-9.00226-2 · Cited by 11 · see citing works
Dhawan, D., Metcalf, M., & Zgid, D. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00931 · Cited by 4 · see citing works
Quantum-like Data Modeling in Applied Sciences: Review
Lipovetsky, S. (2023) · Stats · doi:10.3390/stats6010021 · Cited by 4 · see citing works
Ground state property calculations of LiHn complexes using IBM Qiskit’s quantum simulator
Avramidis, B., Paudel, H. P., Alfonso, D. R., Duan, Y., & Jordan, K. D. (2024) · AIP Advances · doi:10.1063/5.0188249 · Cited by 4 · see citing works
Chemically motivated simulation problems are efficiently solvable on a quantum computer
Schleich, P., Kristensen, L. B., Campos-Gonzalez-Angulo, J. A., Aldossary, A., Avagliano, D., Bagherimehrab, M., Gorgulla, C., Fitzsimons, J., & Aspuru‐Guzik, A. (2025) · Digital Discovery · doi:10.1039/d5dd00377f · Cited by 9 · see citing works
Artificial Intelligence and Cloud Computing in Healthcare
Manokaran, S., Vyshnavi, T., Maiya, U. C., Shashidhara, A., Reddy, A. H. M., & Basori, A. H. (2026) · Signals and communication technology · doi:10.1007/978-3-032-03554-7_5 · Cited by 2 · see citing works
Quantum Computing: Navigating The Technological Landscape for Future Advancements
Rawat, M., Mahajan, J., Jain, P., Banerjee, A., Oza, C., & Saxena, A. (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545252 · Cited by 2 · see citing works
Zhao, L., Goings, J. J., Aboumrad, W., Arrasmith, A., Calderín, L., Churchill, S., Gabay, D., Harvey-Brown, T., Hiles, M. L., Kaja, M., et al. (2026) · Physical Review Research · doi:10.1103/n1tf-8kr7 · Cited by 7 · see citing works
Preparing general mixed quantum states on quantum computers
Friedrich, L., Pinto, D. F., Chrysosthemos, D. S. S., & Maziero, J. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05299-7 · Cited by 1 · see citing works
Karacan, E., Chen, Y., & Mendl, C. B. (2025) · Quantum · doi:10.22331/q-2025-02-06-1624 · Cited by 1 · see citing works
Gresch, A., Tepe, U., & Kliesch, M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00370 · Cited by 2 · see citing works
“[3, 4] for an overview.”
Determining the Ensemble N -Representability of Reduced Density Matrices
Oña, O. B., Massaccesi, G. E., Capuzzi, P., Laín, L., Torre, A., Peralta, J. E., Alcoba, D. R., & Scuseria, G. E. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01788 · Cited by 1 · see citing works
SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range
Günkel, T., Barrera, A., Balcells, L., Mestres, N., Miranda, E. A., Palau, A., & Suñé, J. (2026) · Advanced Electronic Materials · doi:10.1002/aelm.202500861 · Cited by 1 · see citing works
Learning Quantum System Disturbance Models with Probabilistic Bayesian Neural Networks
Jiang, Z., & Nguyen, L. (2023) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon58068.2023.10365822 · Cited by 1 · see citing works
Ileana, M., Petrov, P., & Milev, V. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101908 · Cited by 1 · see citing works
“Such technologies would allow the creation of highly dynamic profiles that take into account multiple influencing factors simultaneously - interests, learning habits, and interaction with content [28].”
Quantum-level machine learning calculations to predict the PES of SelegilinePreprint
Shirani, H., & Hashemianzadeh, S. M. (2024) · Research Square · doi:10.21203/rs.3.rs-3979440/v1
Modelling Heterogeneous Catalysis using Quantum Computers: An academic and industry perspectivePreprint
Hariharan, S., Kinge, S., & Visscher, L. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-d2l1k
Enhancing Metaheuristics: The Role of Quantum-Inspired Soft Computing
Das, P., Jain, C., Ansul, Gola, K. K., Kumar, N., & Singh, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-87931-9_8
Applications of Gaussian Boson Sampling to Solve Some Chemistry Problems
Novir, S. B. (2025) · Quantum Reports · doi:10.3390/quantum7040056
“Quantum computers, because of their quantum mechanical properties, can simulate quantum systems effectively and provide exponential improvements compared with classical computing for specific computational problems [9,12–14].”
Solving Helmholtz problems with finite elements on a quantum annealer
Rémi, A., Damanet, F., & Geuzaine, C. (2025) · Physical Review A · doi:10.1103/l4l6-qlj4
Perrier, E., & Aboy, M. (2026) · Perspectives in law, business and innovation · doi:10.1007/978-981-92-3768-5_3
Nelluri, K. D. D., Rahaman, S. A., Kommoju, M., & Alaparthi, B. (2025) · De Gruyter eBooks · doi:10.1515/9783111681870-009
Quantum Leap: Unveiling the Power of Quantum Computing for Environmental Science
Pooranam, N., Surendran, D., Narmadha, R. P., Aruna, N., & Nisha, M. R. (2026) · Springer eBooks · doi:10.1007/978-3-032-15188-9_5
Calculating the binding energies of the first 29 light nuclei with VQE on quantum computing
Baskan, T., & Yılmaz, A. H. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae4dc7
Sree, S. R., Sandhya, M., Jayanthi, A., Pulipati, V., Rao, D. M. V., & Subrahmanyam, D. V. S. S. (2026) · Discover Computing · doi:10.1007/s10791-026-10240-8
“Liu and Troyer [23] and Andersson et al. Ni et al. [17] highlighted hybrid pipelines integrating molecular dynamics [9], QML [23], and classical HPC for drug design.”
InfluentialComputing the Molecular Ground State Energy in a Restricted Active Space Using Quantum Annealing
Bruni, S., & Prati, E. (2026) · Advanced Quantum Technologies · doi:10.1002/qute.70368
“The development of such quantum computing approach becomes therefore essential for advancing the accurate modeling of molecules and design of materials of practical and industrial significance [ 8 ].”
Quantum-Assisted Drug Discovery for Lung Cancer
Thopate, K., Naik, N., Patil, A., Pawar, A., & Pokale, T. (2026) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt67648.2026.11681175
Qubit Readout Discrimination through Fuzzy Clustering
Acampora, G., Schiattarella, R., & Vitiello, A. (2026) · ACM Transactions on Quantum Computing · doi:10.1145/3847660
- Authors
- Liu, H., Low, G. H., Steiger, D. S., Häner, T., Reiher, M., & Troyer, M.
- Affiliations
- Microsoft (United States) (US)
- Microsoft (Switzerland) (CH)
- ETH Zurich (CH)
- Corresponding author
- Reiher, Markus
- Troyer, Matthias
- Venue
- Materials Theory
- Published
- 2022-03-07
- Volume
- 6
- Issue
- 1
- ISSN
- 2509-8012 (ISSN-L)
- OpenAlex
- W3129699569
- Cited by
- 73
- References
- 120
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 20 · 2023: 10 · 2022: 8 · 2021: 3
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Materials Theory (publishedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- 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 computing, quantum chemistry, molecular dynamics, wave function representation, qubits, molecular physics, chemical synthesis
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 ›
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